Coordinated processing of data by networked computing resources

ABSTRACT

Systems, methods, and computer-readable media for coordinating processing of data by multiple networked computing resources include monitoring data associated with a plurality of networked computing resources, and coordinating the routing of data processing segments to the networked computing resources.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims all benefit, including priority, of:

-   -   U.S. Provisional Patent Application No. 62/126,106, filed Feb.        27, 2015, and entitled COORDINATED PROCESSING OF DATA BY        NETWORKED COMPUTING RESOURCES;    -   U.S. Provisional Application No. 62/126,120, filed Feb. 27,        2015, and entitled COORDINATED PROCESSING OF DATA BY NETWORKED        COMPUTING RESOURCES; and    -   U.S. Provisional Application No. 62/132,063, filed Mar. 12,        2015, and entitled COORDINATED PROCESSING OF DATA BY NETWORKED        COMPUTING RESOURCES.

This is application is also a continuation-in-part of U.S. patentapplication Ser. No. 12/796,139, filed Jun. 8, 2010, and entitledSYNCHRONIZED PROCESSING OF DATA BY NETWORKED COMPUTING RESOURCES; whichclaims the benefit of U.S. Provisional Patent Application No.61/285,375, filed Dec. 10, 2009, and entitled SYNCHRONIZED PROCESSING OFDATA BY NETWORKED COMPUTING RESOURCES.

All of the above-noted applications are hereby incorporated by referencein their entireties.

TECHNICAL FIELD

The present disclosure relates generally to systems, methods, devicesand computer-readable media for the management of data processing bymultiple networked computing resources. In particular, the disclosurerelates to the coordination or synchronization of related or temporalrequests for processing of data at distributed network resources.

Aspects of the material disclosed in this application may underlie orrelate to the holding, transfer, and/or administration of securities andother financial interests. Aspects of such holding, transfer, and/oradministration may be subject to regulation by governmental and otheragencies. The disclosure herein is made solely in terms of logical,programming, and communications possibilities, without regard tostatutory, regulatory, or other legal considerations. Nothing herein isintended as a statement or representation that any system, method orprocess proposed or discussed herein, or the use thereof, does or doesnot comply with any statute, law, regulation, or other legal requirementin any jurisdiction; nor should it be taken or construed as doing so.

BACKGROUND

In various forms of networked or otherwise distributed data processingsystems, complex and/or multiple related processes are often routed tomultiple computing resources for execution. For example, in financialand other trading systems, orders for purchases, sales, and othertransactions in financial interests are often routed to multiple marketor exchange servers for fulfillment. For example, when a large order isrouted to multiple exchanges (e.g., based on the liquidity available ineach market), orders tend to arrive at the faster exchanges (i.e., thosehaving fewer inherent latencies) before they arrive at slower exchanges(i.e., those having greater inherent latencies), and thus show in thebooks of different exchanges at different times. When orders begin toshow on the books of the faster exchanges, other parties can detect theorders and attempt to take advantage of the latency in slower exchangesby cancelling, changing, and or otherwise manipulating quotes (e.g.,bids and offers) or other market parameters on the slower exchanges,effectively increasing the implicit trading costs. As a result, ordersthat may have otherwise executed on any single exchange at a high fillratio tend to exhibit a lower overall fill ratio when routed to multipleexchanges as a split trade.

Prior art documents, such as the Rony Kay article “Pragmatic NetworkLatency Engineering, Fundamental Facts and Analysis, have attempted toaddress such problems by proposing elimination of one-way communications(i.e., “packet”) latencies. Such systems fail to address arbitrageopportunities and other issues caused or facilitated by variations inthe time required for multiple processors to execute individual portionsof multiple-processor execution requests (i.e., execution latencies), inaddition to (or as part of) communications latencies.

SUMMARY

In various aspects the present disclosure provides systems, methods, andcomputer-executable instruction mechanisms (e.g., non-transientmachine-readable programming structures) such as software-codedinstruction sets and data, for the management of data processing bymultiple networked computing resources. In particular, for example, thepresent disclosure provides systems, methods, and media useful incontrolling the synchronization or coordination of related requests forprocessing of data using distributed network resources.

For example, in one aspect the present disclosure provides systems,methods, and media for coordinating processing of data by multiplenetworked computing resources. Such systems, for example, can include atleast one processor configured to: receive from one or more data sourcessignals representing instructions for execution of at least one dataprocess executable by a plurality of networked computing resources;divide the at least one data process into a plurality of data processingsegments, each data processing segment to be routed to a different oneof a plurality of networked execution processors; based at least partlyon latencies in execution of prior data processing requests routed bythe system to each of the plurality of networked execution processors,determine a plurality of timing parameters, each of the plurality oftiming parameters to be associated with a corresponding one of theplurality of data processing segments, the plurality of timingparameters determined to cause synchronized execution of the pluralityof data processing segments by the plurality of networked executionprocessors; route the plurality of data processing segments to theplurality of corresponding networked execution processors in a timingsequence based on the timing parameters; determining a capture ratio forthe data processing segments; and adjusting the timing parametersassociated with each of the plurality of networked execution processorsbased on the capture ratio.

In some aspects the present disclosure provides systems, methods, andprogramming or other machine-interpretable instructions for causingsynchronized/coordinated processing of data by multiple networkedcomputing resources, such systems, for example, comprising at least oneprocessor configured to execute machine-interpretable instructions andcausing the system to: monitor execution of signal processing executionrequests by each of the plurality of networked computing resources;determine at least one timing parameter associated with a latency inexecution of signal processes between the system and each of theplurality of networked computing resources; and store the at least onetiming parameter in machine-readable memory accessible by the at leastone processor.

Monitoring of execution of signal processing execution requestsaccording to such and other embodiments of the invention can beimplemented on continual, periodic, and/or other suitable or desirablebases.

In various embodiments of the various aspects of the invention, thenetworked computing resources can include one or more exchange servers.The data sources can include one or more broker or trader systems orservers, the controlled signal processes can represent trades infinancial interests, and the execution of signal processing executionrequests represents the execution of transactions in financialinterests, including for example stocks, bonds, options and contractinterests, currencies and/or other intangible interests, and/orcommodities. In such embodiments requests for execution of dataprocessing procedures can be based wholly or partially on parametersincluding, for example, any one or more of current market dataquotations, order routing rules, order characteristics, displayedliquidity of each networked computing resource, and a probable delay, orlatency, in execution of an order quantity at each networked computingresource.

In the same and further aspects the invention provides systems forcontrolling or otherwise managing requests for processing of data bydistributed computer resources, such systems including one or moreprocessors configured to execute instructions for causing the system to:monitor execution of signal processing execution requests by each of theplurality of networked computing resources; determine at least onetiming parameter associated with the latency in execution of signalprocesses between the system and each of the plurality of networkedcomputing resources; and store the at least one timing parameter foreach of the plurality of networked computing resources.

In accordance with one aspect, there is provided a system forcoordinating processing of data by multiple networked computingresources. The system includes at least one processor configured to:monitor data associated with a plurality of networked computingresources, the monitored data including data associated with dataprocessing segments previously routed to the plurality of networkedcomputing resources; receive from one or more data sources signalsrepresenting instructions for execution of at least one data processexecutable by the plurality of networked computing resources; based onthe monitored data: divide the at least one data process into at leastone data processing segment, each data processing segment to be routedto one of the plurality of networked computing resources; determine aplurality of timing parameters, each of the plurality of timingparameters to be associated with a corresponding one of the plurality ofnetworked computing resources, the plurality of timing parametersdetermined to cause synchronized execution of the at least one dataprocessing segment by the plurality of networked computing processors;and route the at least one data processing segment to the plurality ofcorresponding networked computing processors in a timing sequence basedon the timing parameters.

In accordance with another aspect there is provided a method forcoordinating processing of data by multiple networked computingresources. The method includes: monitoring data associated with aplurality of networked computing resources, the monitored data includingdata associated with data processing segments previously routed to theplurality of networked computing resources; receiving from one or moredata sources signals representing instructions for execution of at leastone data process executable by the plurality of networked computingresources; based on the monitored data: dividing the at least one dataprocess into at least one data processing segment, each data processingsegment to be routed to one of the plurality of networked computingresources; determining a plurality of timing parameters, each of theplurality of timing parameters to be associated with a corresponding oneof the plurality of networked computing resources, the plurality oftiming parameters determined to cause synchronized execution of the atleast one data processing segment by the plurality of networkedcomputing processors; and routing the at least one data processingsegment to the plurality of corresponding networked computing processorsin a timing sequence based on the timing parameters.

In accordance with another aspect there is provided a computer-readablemedium or media having stored thereon instructions which when executedby at least one processor, configured the at least one processor to:monitor data associated with a plurality of networked computingresources, the monitored data including data associated with dataprocessing segments previously routed to the plurality of networkedcomputing resources; receive from one or more data sources signalsrepresenting instructions for execution of at least one data processexecutable by the plurality of networked computing resources; based onthe monitored data: divide the at least one data process into at leastone data processing segment, each data processing segment to be routedto one of the plurality of networked computing resources; determine aplurality of timing parameters, each of the plurality of timingparameters to be associated with a corresponding one of the plurality ofnetworked computing resources, the plurality of timing parametersdetermined to cause synchronized execution of the at least one dataprocessing segment by the plurality of networked computing processors;and route the at least one data processing segment to the plurality ofcorresponding networked computing processors in a timing sequence basedon the timing parameters.

In accordance with another aspect there is provided: a system forcoordinating processing of data by multiple networked computingresources. The system includes at least one processor configured to:obtain a plurality of data processing waves, each data processing waveidentifying: one or more data processing segments, one or more networkedcorresponding computing resources to which the one or more dataprocessing segments are to be routed; and a timing sequence in which theone or more data processing segments are to be routed; obtain a minimumhandling interval for each of the networked computing resources;schedule an order for routing the plurality of data processing wavesbased on the wave timing sequences and the minimum handling intervalsfor the networked computing resources; and route each of the dataprocessing segments in the plurality of data processing waves based onthe order.

In accordance with another aspect, there is provided: a method forcoordinating processing of data by multiple networked computingresources, the method comprising: obtaining a plurality of dataprocessing waves, each data processing wave identifying: one or moredata processing segments, one or more networked corresponding computingresources to which the one or more data processing segments are to berouted; and a timing sequence in which the one or more data processingsegments are to be routed; obtaining a minimum handling interval foreach of the networked computing resources; scheduling an order forrouting the plurality of data processing waves based on the wave timingsequences and the minimum handling intervals for the networked computingresources; and routing each of the data processing segments in theplurality of data processing waves based on the order.

In accordance with another aspect, there is provided: acomputer-readable medium or media having stored thereon instructionswhich when executed by at least one processor, configure the at leastone processor to: obtain a plurality of data processing waves, each dataprocessing wave identifying: one or more data processing segments, oneor more networked corresponding computing resources to which the one ormore data processing segments are to be routed; and a timing sequence inwhich the one or more data processing segments are to be routed; obtaina minimum handling interval for each of the networked computingresources; schedule an order for routing the plurality of dataprocessing waves based on the wave timing sequences and the minimumhandling intervals for the networked computing resources; and route eachof the data processing segments in the plurality of data processingwaves based on the order.

In accordance with another aspect, there is provided: a system forcoordinating processing of data by multiple networked computingresources, the system comprising at least one processor configured to:receive from one or more data sources signals representing instructionsfor execution of a plurality of data processes executable by a pluralityof networked computing resources, the data processes representing aproposed trade in a financial interest; obtaining data associated withavailable liquidity of the financial interest at each of the pluralityof networked computing resources; divide each of the plurality of dataprocesses into a plurality of data processing segments, each dataprocessing segment divided from a single data process to be routed to atleast one of the plurality of networked computing processors; based atleast partly on latencies in execution of prior data processing requestsrouted by the system to each of the plurality of networked computingprocessors and the available liquidity at each of the plurality ofnetworked computing processors, determine a plurality of timingparameters, each of the plurality of timing parameters to be associatedwith a corresponding one of the plurality of data processing segments,the plurality of timing parameters determined to cause synchronizedexecution of the plurality of data processing segments by the pluralityof networked computing processors; based on the timing parameters andnetworked computing processors associated with each of the plurality ofdata processes, determine a timing sequence for routing the dataprocessing segments for all of the plurality of data processes; androute the plurality of data processing segments to the plurality ofcorresponding networked computing processors based on the timingsequence.

In accordance with another aspect, there is provided a method forcoordinating processing of data by multiple networked computingresources, the method: receiving from one or more data sources signalsrepresenting instructions for execution of a plurality of data processesexecutable by a plurality of networked computing resources, the dataprocesses representing a proposed trade in a financial interest;obtaining data associated with available liquidity of the financialinterest at each of the plurality of networked computing resources;dividing each of the plurality of data processes into a plurality ofdata processing segments, each data processing segment divided from asingle data process to be routed to at least one of the plurality ofnetworked computing processors; based at least partly on latencies inexecution of prior data processing requests routed by the system to eachof the plurality of networked computing processors and the availableliquidity at each of the plurality of networked computing processors,determining a plurality of timing parameters, each of the plurality oftiming parameters to be associated with a corresponding one of theplurality of data processing segments, the plurality of timingparameters determined to cause synchronized execution of the pluralityof data processing segments by the plurality of networked computingprocessors; based on the timing parameters and networked computingprocessors associated with each of the plurality of data processes,determining a timing sequence for routing the data processing segmentsfor all of the plurality of data processes; and routing the plurality ofdata processing segments to the plurality of corresponding networkedcomputing processors based on the timing sequence.

In accordance with another aspect, there is provided a computer-readablemedium or media having stored thereon instructions which when executedby at least one processor, configure the at least one processor to:receive from one or more data sources signals representing instructionsfor execution of a plurality of data processes executable by a pluralityof networked computing resources, the data processes representing aproposed trade in a financial interest; obtaining data associated withavailable liquidity of the financial interest at each of the pluralityof networked computing resources; divide each of the plurality of dataprocesses into a plurality of data processing segments, each dataprocessing segment divided from a single data process to be routed to atleast one of the plurality of networked computing processors; based atleast partly on latencies in execution of prior data processing requestsrouted by the system to each of the plurality of networked computingprocessors and the available liquidity at each of the plurality ofnetworked computing processors, determine a plurality of timingparameters, each of the plurality of timing parameters to be associatedwith a corresponding one of the plurality of data processing segments,the plurality of timing parameters determined to cause synchronizedexecution of the plurality of data processing segments by the pluralityof networked computing processors; based on the timing parameters andnetworked computing processors associated with each of the plurality ofdata processes, determine a timing sequence for routing the dataprocessing segments for all of the plurality of data processes; androute the plurality of data processing segments to the plurality ofcorresponding networked computing processors based on the timingsequence.

As will be appreciated by those skilled in the relevant arts, once theyhave been made familiar with this disclosure, coordination orsynchronization of execution of distributed data processing requests by,for example, synchronized or coordinated transmission of requests forsuch processing, has a great many possible applications in a largenumber of data processing fields.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made to the drawings, which show by way of exampleembodiments of the present disclosure.

FIGS. 1A, 1B, 1C, 3 and 9 show examples of systems suitable for causingprocessing of data by multiple networked computing resources inaccordance with various aspects of the invention.

FIGS. 2, 4, 8 and 12 show flowcharts illustrating examples of methodsfor coordinating processing of data by multiple networked computingresources in accordance with various aspects of the invention.

FIG. 5 shows an example histogram that may be used in an example methodfor managing processing of data by multiple networked computingresources in accordance with various aspects of the invention.

FIGS. 6A and 6B show a comparison of fill ratios using an example methodand system for processing of data by multiple networked computingresources versus using a conventional method and system.

FIG. 7 illustrates the use of an example metric for comparing an examplemethod and system for processing of data by multiple networked computingresources versus results of using a prior art method and system.

FIG. 10 shows a table illustrating example data associated withnetworked computing resources.

FIG. 11A, 11B, 11C, 11D, 11E show example schedules for routing dataprocessing segments in data processing waves.

Throughout the appended drawings, like features are identified by likereference numerals.

DESCRIPTION OF EXAMPLE EMBODIMENTS

In this disclosure, as will be understood by those skilled in therelevant arts, ‘synchronized’ or ‘coordinated’ can mean according to anydesired timing sequence, whether regular, irregular, and/or wholly orpartially simultaneous.

FIG. 1 shows an example of a system 100 suitable for coordinatingprocessing of data by multiple networked computing resources inaccordance with the invention.

In the example shown, system 100 includes one or more signal or datasources 102 (comprising one or more each of sources 102 a, 102 b),execution router processor(s) 104, and one or more networked computingresources, or execution processors, 106. In some embodiments, datasources 102 may include one or more internal data sources 102 a, whichmay communicate with the router 104 directly (e.g., through privatelocal- or wide area network(s) or other secure wireless or wirelinecommunication, through direct communication channel(s) or throughcommunication(s) within a single server). In the same and/or otherembodiments, data source(s) 102 may also include one or more externaldata sources 102 b, which may for example communicate with routerprocessor(s) 104 via one or more public networks 108 (e.g., a public orprivate telecommunications network such as the internet), using suitableor otherwise desired network security devices, which may for exampleinclude data encryption, etc. In the example shown, router processor(s)104 communicate with each of the one or more networked execution, orcomputing, resources 106 via a network 110, which may be the same as ordifferent than network(s) 108.

In various embodiments, data source(s) 102 may include devices thatprovide, on behalf of one or more entities that generate trading and/orother data processing requests, signals that communicate data and/orinstructions related to execution of data processing processes to routerprocessor(s) 104, which data and/or instructions the router processor(s)104 may process (e.g., aggregate by summing, averaging, etc.; and/ordivide into segments, etc.) and use as bases for requests for processingof data by the networked computing resources 106. Data sources 102 a,102 b may include, for example, systems, servers, processors and/or anyother suitable source(s) of requests for execution of data processingtasks such as offers and/or bids for purchase of commodities, intangiblefinancial interests, etc., and/or other data processing tasks, such asword, image, and/or other communications or document processing tasks.Each or any of data source(s) 102, processor(s) 104, and resources 106may include multiple such systems, servers or processors.

In various embodiments, some or all of data source(s) 102 and routerprocessor(s) 104 may be combined, and/or otherwise configured toimplement multiple programming or other machine instruction applicationsrunning on single machines.

Networked computing resources 106 may include any devices or otherresources that communicate with router processor(s) 104 to receive andcarry out any of a very wide variety of data processing requests. Suchnetworked computing resources 106 may include systems, servers,processors or any other suitable devices adapted for execution of anyprocesses suitable for use in implementing the invention, including, forexample, processing of offers or bids for purchase of commodities,financial interests, etc., and/or other data processing tasks, such asword or document processing, image, and/or other communications ordocumentation tasks.

In various embodiments, the one or more data sources 102 transmit orotherwise provide to or for the router processor(s) 104 signalsrepresenting instructions, or requests, for executing data processingfunctions. Instructions from any given data source(s) 102 may includeinstructions for signal processes to be executed by any one or morenetworked computing resources 106. Requested signal processes mayinclude, for example, computing operations, data manipulations, and/orcommunications processes or other signal exchanges, among others. Insome but not necessarily all examples, such instructions mayspecifically identify networked computing resource(s) 106 particularlytargeted for execution of such processes.

Router processor(s) 104 may parse instruction signals received from oneor more source(s) 102 and use such signals to prepare instructions, orrequests, to be forwarded to pluralities of execution processors 106,for execution of data processing and/or other signal processes inaccordance with the received instructions. Parsing of such instructionsmay include, for example, identifying the type of process(es) to berequested, including for example the volume or quantity of an order orbid for a trade or an amount of document processing to be done, and thetype, nature, and/or identity(ies) of networked computing resource(s)106 to be requested to execute, and thereby associated with, a givendata processing and/or other signal processing request.

For example, in order to increase the efficiency of signal and/or otherdata processing functions, router processor(s) 104 may parse, sort, andaggregate instructions or requests received from multiple sources 102for relatively smaller execution requests into one or more largerrequests for processing, and further divide such aggregated request(s)into pluralities of smaller requests to be distributed to plurality(ies)of execution processors 106, depending, for example, on the currentability of the execution processors 106 to satisfy or complete suchprocessed requests.

For example, multiple instruction signal sets received from differentdata sources 102 a, 102 b may be associated with (e.g., addressed fordelivery to and execution by) individual networked computing resource(s)106, and such instructions may be aggregated into single signal processexecution requests for such networked computing resource(s) 106. In someexamples, identification of the networked computing resource(s) 106 tobe tasked with a given signal processing request may be performed afterthe aggregating. For example, multiple instructions from different datasources 102 a, 102 b may be sorted or otherwise associated with a singlesignal or data process, and such instructions may be aggregated, and theaggregated instructions may be associated with one or more identifiednetworked computing resource(s) 106, such that one or more signalprocess requests may be accordingly prepared for the identifiednetworked computing resource(s) 106. Such parsing, sorting, and/oridentification may be performed according to predetermined rules oralgorithms (e.g., based on continuing or current processing capabilitiesof one or more specific networked computing resource(s) 106), andaccording to requirements encoded in the instructions or otherwiseprovided by the originating source(s) 102, where relevant.

As a further example, single instruction sets for processing of data maybe broken down by processor(s) 104 and distributed to a plurality ofresources 106 for distributed execution. For example, a relatively largeorder for trading in one or more financial interests originating from asingle source 102 a, 102 b, might need to be distributed to multipleexchange servers 106 in order to be completely filled; in such casesrequest(s) from one or more source(s) 102 may be broken down byprocessor(s) 104 into suitable orders for execution by a plurality ofsuch resources 106.

In some embodiments, the instruction sets may be received as parts ofvarious order waves from one or more brokers. These order waves maycontain instruction sets, which may be targeted to be transmitted tovarious venues for trading in one or more financial interests.

Targeted, or specifically identified, networked computingresources/execution processors 106 communicate with the routerprocessor(s) 104 to receive the segmented signal process executionrequests and may thereafter execute them accordingly. Execution of suchsignal processes may include, for example, carrying out a text- orimage-processing operation, a mathematical computation, or acommunications signal exchange, among others.

As will be readily understood by those skilled in the relevant arts,various components of system 100 may combined, or may be implemented inthe form of separate systems or devices. In a wide variety ofconfigurations, such combined or separate (sub)systems may be operatedby the same or distinct entities. As a particular example, one or morerequest source(s) 102 may be integrated with, or otherwise associatedwith, individual router(s) 104.

In some embodiments, the system may provide one or more intelligentorder routers 104 including one or more processors that may beconfigured for the sequenced, prioritized, scheduled, staggered,segmentation, and/or grouped routing of orders or other data processingsegments/requests related to one or more financial interests.

For example, data processing segments may be grouped in waves, and therouting of the individual segments may be scheduled, staggered, grouped,etc., based on the monitored data associated with destination networkedcomputing resources. In some embodiments, the data processing segmentsmay be routed such that the orders arrive at, are processed by, and/orare executed at one or more networked computing resources within atimeframe defined at least in part on the monitored data. FIG. 1Cprovides a sample schematic diagram depicting a number of example wavesof data processing segments being submitted by Brokers X and Y to Venues1-5, using router processor(s) 104.

In some embodiments, multiple instances of a data processing request maybe routed to multiple exchanges with different quantities, conveyed in adetermined timing sequence. The timing sequence may be based on timingparameters determined from the monitored data, and may define a timedorder in which the initiation of the routing of the data processingsegments should be performed.

In routing orders, the system may be configured for the determination oftiming parameters, which may include the determination of timing ranges,distributions, etc. The timing parameters may be variable, adaptive,weighted, probabilistic, etc., and may also be tunable depending onvarious factors, such as network congestion, order load, order priority,venue characteristics, etc.

In some embodiments, the system may be configured to adapt the routingof orders (data processing segments) based on predicted order flow. Forexample, the system may have a large number of scheduled orders to berouted at a particular time or during a particular timeframe.Accordingly, the system may be configured to adapt the routing of one ormore orders given predicted loads on networking equipment, communicationchannels, venue systems, routing pathways, intermediary networkequipment, etc. The system may be further configured to distribute therouting of orders across various network links and/or across differenttimeframes in order to load-balance the routing of orders.

As described herein, the system monitors data associated with one ormore networked computing resources. This data can be acquired with orreceived from with one or more components or devices in the system.

The system may also be configured for monitoring network data associatedwith one or more networked computing resources, including monitoring ofnetwork performance, monitoring of available network links, amongothers. Probabilistic distributions and/or models of network performancemay be generated, adapted, defined and/or employed in relation tonetwork monitoring. Network monitoring may be utilized for adapting timeparameters, etc. Network monitoring may include the determination and/ormonitoring of, for example, latency means, maximums, standard deviation,kurtosis, L-kurtosis, skewing, medians, variance, mode, correlations,cross-correlations, covariance, etc., and may be correlated and/orassociated with other factors, such as order load, time of day (e.g.,the opening of trading, the closing of trading), the occurrence offinancial events (e.g., stop trade orders, corporate events,publications of statistics, publication of analyst reports, creditrating changes), the occurrence of network events (e.g., sunspots,denial of service attacks).

Network monitoring may occur, for example, through the tracking ofnetwork performance through ‘heartbeats’ (e.g., the transmission of pingsignals, regularly scheduled transmissions, echo request packets) tomeasure the transmission time of signals, historical order routingperformance, recent order routing flow, the sending of test messages,etc.

In some embodiments, test messages are utilized for network monitoringand the difference in timing between test messages and routed ordermessages may be utilized in determining various order routingcharacteristics, such as time required to process an order, internallatency within a venue, etc.

In some embodiments, the system utilizes various network predictiontechniques in determining order load across various network links, atvarious venues, etc., including orders scheduled to be routed by thesystem itself. Execution requests may be rearranged and/or scheduledbased in part on the predicted load.

In some embodiments, the system can access database(s) or may receive ormonitor network messages to obtain information regarding the topology ofone or more network connection(s) as well as the redundancy scheme. Forexample, the system may have two or more connections and/or routes to avenue. By accessing and monitoring the performance of these connectionsand/or routes, when a primary connection/route fails, upon detection,the system can be configured to route all subsequent orders based onlatencies associated with a known secondary route/connection so as tominimize undesired timings and likely fill rates caused by the failure.

In some embodiments, when available, network monitoring can be performedon previously routed trade requests.

The system may also be configured for the tracking of the trade captureratio, which may be determined by ratio between the sum of orderliquidity as determined at a time (e.g., when a trade decision is made)and the amount of liquidity that was captured in a trade.

The trade capture ratio may be utilized for various purposes, such asadapting timing parameters, determining that a network component hasfailed, detecting that third parties may be intercepting orders, etc.For example, a high trade capture ratio may be indicative of reducedinformation leakage and/or increased fill rate, and a low trade captureratio may be indicative of increased information leakage and/or adecreased fill rate. A low trade capture ratio may, for example, triggeran alert, a notification, adaption by the system through the applicationof business logic, implementation of timing parameters, modified routingstrategies, etc.

In some embodiments, the system may include business logic that may beutilized in determining how orders, routing instructions, etc., may besequenced, prioritized, scheduled, staggered, segmentation, and/orgrouped. The business logic may also be utilized for route selection,route determination, route prioritization, etc. The business logic mayalso be utilized for decision support. In some embodiments, the businesslogic may be utilized to prioritize orders based on various businessrules, such as rules configured to prioritize venues having order bookscontaining larger amounts of liquidity for a particular financialsecurity, etc.

In some embodiments, the business logic may include intelligent decisionsupport capabilities, such as the ability to traverse decision trees,etc.

In some examples, the system may access, receive or determine (based onmonitored network and order performance) information regarding differentvenues. In some examples, this information may include an estimate ofthe message handling capabilities of venue(s). As illustrated herein, iforder falls into a venue queue or processing capabilities of venue(s)are overloaded, the resulting processing delay may affect theeffectiveness of timing parameters and may reduce the control the systemhas on limiting latency arbitrage. Accordingly, in some examples, thesystem can be configured to use timing parameters to avoid venue queuingor overloading.

In some examples, venue information can include physical locations ofvenues, distances between venues (optical cable distances ortransmission times), known or likely presence of a co-located predatorytrader, effectiveness or speed of the co-located trader, etc. In someexamples, one or more of these factors can be used to determine timingparameters. For example, if an order wave involves a venue with a known,effective co-located trader, the timing parameters may be determinedbased on a lower timing tolerance/differential. In some examples,routing trades with the highest timing tolerance will occur when thetrades are routed in a timing sequence resulting in simultaneous arrivalor execution. In some instances, having simultaneous arrival/executionmay not be optimal for changing markets, so the processor(s) may beconfigured to consider different tradeoffs.

The system may be configured to take into consideration the potentialfor third parties to impact the fulfillment of transactions and/ortrades in financial securities. For example, a high-frequency trader mayutilize information relating to a first order at a first venue todetermine that a second order will take place at a second venue. Thehigh-frequency trader may then make an opportunistic trade prior to thearrival of the second order, potentially impacting the price and/orquantity available to the second order. As a result, the second ordermay not be fulfilled or be fulfilled at a lower quantity, impactingtrade capture and fill rate. As a second example, a high-frequencytrader may utilize information relating to a first order at a firstvenue in cancelling an order at a second venue and replacing the orderwith a higher priced order, in anticipation of the arrival of a secondorder by the entity placing the first order. As a third example, ahigh-frequency trader may utilize information relating to a first orderat a first venue in determining order information (e.g., such as theNational Best Bid and Offer) that may be more up to date than the orderinformation available at one or more venues. The high-frequency tradermay then utilize this information to place opportunistic orders thatcause the execution of transactions where prices and/or quantities maybe suboptimal to counterparties to their orders. For example, mispricedorders being left on order books, etc.

The system may be implemented at various components of a tradingnetwork. For example, the system may be implemented at as part of brokerelectronic systems; as part of a network; as an intermediary gateway andmessage delivery service; as part of venue electronic systems, etc.

For example, if the system is implemented as an intermediary gateway andmessage delivery service, the system may be configured to receive fromone or more brokers a plurality of orders, which may be associated withone or more order waves to one or more venues. The intermediary gatewayand message delivery service may then coordinate the routing, clusteringand/or segmentation of the orders and/or suborders to the one or morevenues. For example, a broker may ‘give-up’ an order and route an orderon the broker's behalf.

In some embodiments, the system is implemented at a broker level. Forexample, a broker may utilize the system for the routing of clientorders. In some embodiments, the system is implemented at a clientlevel. For example, a client may utilize the system for the routing ofits own orders.

The system may be configured for the routing of a large number oforders, and may be designed to for scaling based on the volume oforders. Accordingly, the system may be implemented using varioussuitable technologies. In some embodiments, the system is a softwarebased solution. In some embodiments, the system is an appliance basedsolution. In some embodiments, the system is a combination of bothsoftware and an appliance based solution. Scalability may be importantas the system may need to be able to handle a large number of waves frommultiple brokers simultaneously.

In some embodiments, the system is implemented using distributednetworking technologies, for example, cloud computing techniques. Apotential advantage to using distributed networking technologiesincludes the ability to provision resources to support various instancesof the systems based on order routing volumes. Resiliency and/or highavailability technologies, such as failover systems, managed backups,hot/cold backup schemes may also be utilized to achieve a consistentlevel of service and/or uptime. For example, there may be variousinstances of the system that may be configured to share informationamong each other, which may allow for the re-establishment of losslesssessions to clients and venues in the event of a failure.

Some embodiments of the system may provide various benefits, such asincreased trade capture rate, increased fill rates, reduced informationleakage to third parties, reduced risk of ‘moving the market’, improveddecision making (e.g., achieving a more optimal trade-off between orderliquidity and the risk of information leakage), the ability to utilizetiming information from various sources, the ability to utilize adaptiveand/or probabilistic latency models, the ability to quickly andeffectively determine failures in a network or on network equipment,etc.

Where the system is implemented as an intermediary gateway and messagedelivery service, there may be potential benefits as there is no/reducedintegration required at a venue-level and/or a broker-level, and abroker may be able to utilize the system with reduced need forinvestment into infrastructure, there may be the ability to operate thesystem independently of brokers, financial institutions, clients, and/orvenues. In some embodiments, existing infrastructure, messagingprotocols, networks, etc., may be utilized in conjunction with thesystem. A reduced need for integration and/or adaptation, as well asease of interoperation with existing standards and/or protocols may becommercially valuable in view of regulatory requirements, resiliencyrequirements, security requirements, and the potential expense and/orcomplexity required for system modifications. The ability tointeroperate with existing external systems and/or protocols may lead toincreased adoption and/or broker flow.

In addition or alternatively to determining timing parameters forrouting requests to multiple venues, in some embodiments, aspect(s) ofthe system may be configured to determine timing parameters for routingmultiple requests to the same venue (i.e. in waves or otherwise).

An example of an application of a system 100 for distributed executionof segmented processing requests in accordance with the invention isprovided by a financial system 1000 adapted for processing of requestsfor processing of data representing trades and/or offers for trades, orother transactions, in tangible and/or intangible financial interestssuch as stocks, bonds, currencies (e.g., foreign exchange), variousforms of natural resources or commodities, options, loans, etc. As shownin FIGS. 1A and 1B, for example, in a financial transaction dataprocessing system 1000 in accordance with the invention, signal or datasource(s) 102 may include trader system(s) 1102, which may, for example,include trader/broker systems or servers as well as any other sources ofbids, offers, or other transactions in financial interests such ascurrently provided by known financial trading platforms. In variousembodiments, such trader systems 1102 may be referred to as orderorigination systems.

Order origination systems 1102, 102 a may include systems operated by oron behalf of, for example, entities owned or otherwise controlled byparent or other controlling organizations such as banks or brokeragehouses. Order origination systems 1102, 102 b may, for example, includesystems operated by or on behalf of brokers or other trading entitiesacting on behalf of, for example, individual investors, trading throughor with the assistance of independently-controlled banks, institutionalinvestors, and/or other brokerage houses.

Router processor(s) 104 in such embodiments may include, for example,server(s) or other system(s) 1104 that communicate with trader systems1102, 102, for example through the receipt and transmission of encodedelectronic signals representing requests for processing of datarepresenting execution and/or acknowledgement of transactions infinancial interests; and which communicate with broker, exchange, orother market systems or execution processor(s) 1106 for execution ofsuch transactions. In such embodiments a processor 104 may be referredto as a Smart Order Router or Tactical Hybrid Order Router (in eithercase, “SOR”) 1104, 104. An SOR 1104 may, for example, include one ormore gateway(s) 1122 and/or router(s) 1124 for facilitatingcommunications by router(s) 1104 with one or more trader systems 1102,102 directly (e.g., through wired communication, using one or morededicated communication channel(s), or through communication within asingle server) and/or indirectly (e.g., through wireless communication,through a network 108, 1108 or through an intermediate server). Exchangeor market systems 1106, or other execution processor(s) 106 may be incommunication with SOR(s) 1104 through, for example, a network 110,1110, such as the internet or other public network, which may be thesame as the network 1108.

For an embodiment of a system 100 configured as a financial trading ororder execution system 1000, requested and executed signal processesprovided by source(s) 102 may represent trades or other transactions infinancial interests. Such transactions may include, for example, tradesand/or offers for trades, or other transactions, in financial interestssuch as stocks, bonds, currencies (e.g., foreign exchange), variousforms of natural resources or commodities, options, loans, etc.; andnetworked computing resources 106 may be, for example, exchange servers1106, examples of which may include automatic or electronic marketsystems.

As will be well understood by those skilled in the relevant arts, an SOR(sub)system, or processor, 1104 receiving such transaction requestsignal sets can apply a wide variety of processes to the request(s). Forexample, where the signal sets represent requests for transactions infinancial interests, requested transactions can be aggregated, eitherover time and/or across multiple transaction request sources 1102;and/or processing requests for transactions in one or more interests canbe divided for routing to multiple execution handlers or processors1106, individually or in batches.

The signal sets representing requests for transactions in financialinterests, for example, may be FIX sessions as received from clientbrokers. As examples, two order types or order instructions that may beaccepted include immediate or cancel (IOC) orders, and directed singleorders.

In the context of IOC orders—a broker could send in a set of signalsrepresenting orders that the broker intends to be sent out as a singlewave to a set of exchanges. Where the orders are always IOCs, many otherfunctions are simplified as there may be no need for the handling ofsubsequent cancels or replaces. Accordingly, IOC waves may not need tomaintain affinity and may be free to use available exchange sessions tobest manage congestion.

In the context of directed single orders, a broker client can use thesystem to send directed orders to exchanges, taking advantage of networkconnectivity and exchange sessions. This service can be used by a brokeras a primary order gateway to exchanges or as a backup service (reducingtheir needs for backup connectivity to individual exchanges).

In some examples other order types such as day orders, good tillcancelled orders, or any other order type may be used but may requireadditional tracking to maintain order affinities and results, and caninvolve sending and receiving multiple messages (e.g.replace/modify/cancel orders and acknowledgements) to and from thedifferent venues.

In some embodiments, using direct or immediate-or-cancel orders, may insome instances allow the SOR system to use a more efficient messagingprocess which may reduce order messaging loads and/or processing, and/orincrease messaging throughput.

In various embodiments, as described herein, order source(s) 102, 1102can be implemented together with, or as part of, order router(s) 104,1104. It will be readily understood by those skilled in the relevantarts that any or all of the various components of system(s) 100, 1000,including for example any or all of processor(s) 102, 104, 106, andmethods of operating them in accordance with the disclosure herein, maybe implemented using any devices, software, and/or firmware configuredfor the purposes disclosed herein. A wide variety of components, bothhardware and software, as well as firmware, are now known that aresuitable, when used singly and/or in various combinations, forimplementing such systems, devices, and methods; doubtless others willhereafter be developed.

Examples of components suitable for use in implementing examples ofsystems 100, 1000, and the various processes disclosed herein, includingfor example processes 200 of FIG. 2 and 300 of FIG. 4, include, forexample server-class systems such as the IBM x3850 M2™, the HP ProLiantDL380 G5™ HP ProLiant DL585™, and HP ProLiant DL585 G1™. A wide varietyof other processors, including in some embodiments desktop, laptop, orpalm model systems will serve.

An example of a method 200 for processing of a transaction requestsignal set generated by a transaction request signal source 102, 1102,suitable for implementation by a router processor(s) 104 such as, forexample, an SOR 1104 of a system 1000, is shown in FIG. 2.

Process 200 of FIG. 2 can be considered to start at 202, with receipt byprocessor(s) 104, 1104 of signals representing a request for processingof data such as, for example, a transaction in one or more financialinterests. In embodiments of systems 100, 1000 comprising SOR routingprocessor(s) 1104 adapted to process signals representing requests forexecution of trades and/or other transactions in financial interestsreceived from transaction signal source(s) 1102, signal setsrepresenting requests for execution of transactions in one or morefinancial interests can include signals or signal sets representing, forexample, one or more identifiers representing:

-   -   the source(s) of the request, such as a URL or other network        address or identifier used by or otherwise associated with a        trading system 102, 1102;    -   the interest(s) to be traded or otherwise transacted, such as an        identifier used by one or more exchanges to identify a stock, a        CUSIP number for a bond, a set of currencies to be exchanged,        etc.;    -   a type of transaction (e.g., buy, sell, bid, offer, etc.) to be        executed or requested;    -   one or more quantities (i.e., amounts or volumes) of the        interest(s) to be transacted (including for example any total        and/or reserve quantities); and    -   corresponding price terms.

Further parameters can include, for example, current and/or historical:

-   -   fill probability for multi-part, or segmented, transaction        requests (i.e., the historical proportion of multi-part orders        that result in completed transactions);    -   amounts of spread between, for example, bid and offer prices,        e.g., current and/or relative to historical trends in spread;    -   market volatility in specific interests to be traded, or related        or corresponding interest(s), or related benchmarks or indexes;    -   depth of market book(s), for example current depth relative to        historical trends in depth;    -   reserve quantities;    -   order priority;    -   tolerance for information leakage;    -   maximum latency period (which may, for example, constrain a        maximum size for an associated timing parameter);    -   desired routing path;    -   desired venue;    -   specific routing instructions;    -   display quantities; and    -   display size and backing, for example on buy and/or sell sides.

Some or all of these parameters may be collected as or based onmonitored data associated with one or more networked computingresources.

In other embodiments, such signal sets can comprise content and/oridentifiers representing images, text, or other content or to beprocessed by one or more execution processors 104, 1104, and specificexecution requests.

Where incoming client/broker orders would need to be identified as beingpart of an order wave, in some examples, processor(s) can be configuredto receive FIX message repeat groups, where individual orders may havetags that indicate that the order is the Nth order of a wave of M totalorders.

Alternately, orders can be marked as being the first and last orders ofa wave. In some embodiments, affinity may need to be preserved so thatreplaces and cancels are handled correctly on the same venue sessions asthe original orders.

Among the many types of market systems 1106 suitable with variousembodiments of the invention are alternative trading systems (ATSs) ofthe type known as ‘dark’ exchanges, or ‘dark pools’. Typically, suchexchanges do not openly display market offerings to members of thetrading public. The use of known or predicted reserve quantities can beespecially useful in such embodiments.

Thus an example of a data record to be provided by a source 102, 1102 torequest a transaction in a given interest, on stated terms, can include:

<source (102, 1102) of request> <type of transaction> <interest     identifier> <quantity(ies)> <price term(s)>

Signal sets received by processors 104, 1104 at 202 can be stored in anyvolatile and/or persistent memory(ies), as appropriate, for archivaland/or further processing purposes.

At 204, transaction or other data processing execution requests receivedat 202 can be parsed by router processor(s) 104, 1104 to place them inany suitable or desired form for use in preparing one or moreinstruction signal sets to be provided to execution processor(s) 106,1106. Parsing of instruction signals may include, for example,identifying the type of transaction(s) or process(es) to be requested,including for example volumes and/or quantities of orders or bids fortrades in specified interest(s), and whether such volumes are to bebought or sold, or offered for sale or purchase; amounts and/or types ofdocument processing to be done; and the type and nature of networkedcomputing resource(s) or execution processor(s) 106 to be requested toexecute and thereby be associated with such execution or processinginstructions. In various embodiments parsed instruction sets can bestored in temporary or volatile memory(ies) 118, 1018 accessible by thecorresponding processor(s) 104, 1104 for aggregation with otherprocessing requests, division for routing to multiple executionprocessors/resources 106, 1106, and/or preparation and forwarding ofbatch or other delayed-execution requests.

In preparing one or more instruction signal sets, an order schedulingmodule may be utilized that may be configured to handle downstreamcapacity, queuing, and congestion in the network, exchange gateways, andexchange crossing engine queues.

The module may be configured to anticipate the maximum message/orderflow rate that each exchange session can handle, and then pro-activelyavoid exceeding or coming close to that rate. The SOR routerprocessor(s) 1104 may be configured to re-sequence or delay sending outthe early orders in a wave, if it calculates subsequent orders in thewave or in a following wave could become congested or queued.

The module may be configured with a feedback mechanism (based on ACKround trip latency monitoring) to dynamically determine downstreamcongestion and queuing delays as these may not be static, consistent, orpredictable.

To handle these capacities, the SOR router processor(s) 1104 may beconfigured to access exchanges using multiple exchange order sessionsand may also be configured to load balance the flow across the ordersessions.

In some embodiments, instruction sets may consist of FIX messages havingbuilt-in mechanisms such as repeating groups having groups of order infostored as multiple constituents within a single message. These messagesmay often be used to convey waves of baskets—for example, to representorders of the same stock at different venues, or to represent aportfolio that wants to buy a set of 38 stocks, multiple instances ofthe same stock order may be transmitted to multiple exchanges withdifferent quantities, conveyed in unison with the SOR routerprocessor(s) determining scheduling using timing.

Instructions received at 202 may be accumulated during defined timeintervals, regular or irregular, such as the duration of a business dayor any segment thereof, or any other desired time period(s), which maybe preset and/or dynamically determined by processor(s) 104, 1104.Instructions may be also be processed individually, as received. If moreinstructions are to be received prior to processing, or may potentiallybe received, process 200 can return to 202.

Transaction requests/instructions may be accumulated during defined timeintervals, such as the duration of a business day or any segmentthereof, or a desired time period, which may be preset and/ordynamically determined by processor(s) 104, 1104. If more instructionsto be received, or may potentially be received, process 200 can returnto 202.

In embodiments of the invention which employ sorting/aggregationtechniques in parsing or otherwise preparing order or other processingrequests, at 206 processor(s) 104, 1104 can repeat process 202-204 untilall needed or desired related or aggregatable processing request signalsets have been received from source(s) 102, 1102. For example, asdescribed above, arbitrary numbers of data records representing ordersor requests for purchase of bonds identifiable by CUSIP (Committee onUniform Security Identification Procedures) numbers can be received fromdata source(s) 102, 1102, and stored in memory 118, 1018 associated withthe processor(s) 104, 1104, for batch processing, for example:

<source 1> <sell> <CUSIP No. AA> <10,000> <price A> <res. 9,000> <priceD> <source 2> <buy> <CUSIP No. BB> <12,000> <price C> <res. 1,000><price B> <source 3> <sell> <CUSIP No. BB> <11,000> <price A> <res.8,000> <price D> <source 6> <sell> <CUSIP No. AA> <14,000> <price A><res. 2,000> <price E> <source 4> <buy> <CUSIP No. AA> <18,000> <priceC> <res. 7,000> <price B> <source 1> <sell> <CUSIP No. BB> <20,000><price A> <res. 3,000> <price D> <source 3> <sell> <CUSIP No. AA><13,000> <price A> <res. 6,000> <price D> <source 4> <buy> <CUSIP No.BB> <22,000> <price C> <res. 4,000> <price B> <source 5> <sell> <CUSIPNo. AA> <21,000> <price A> <res. 5,000> <price E> <source 4> <buy><CUSIP No. BB> <15,000> <price C> <res. 7,000> <price F> <source 1><sell> <CUSIP No. AA> <19,000> <price A> <res. 3,000> <price D> <source5> <buy> <CUSIP No. BB> <16,000> <price C> <res. 8,000> <price F><source 6> <sell> <CUSIP No. BB> <17,000> <price A> <res. 6,000> <priceH>

In another example scenario, processor(s) can receive individual largeexecution instructions from data source(s) 102, 1102. For example:

-   -   <source 1><buy><CUSIP No. AAA><100,000><price A>

In another example scenario, as illustrated by example in FIG. 1C,processor(s) can receive order execution instructions in waves. Asillustrated in the example instructions in FIG. 1C, in some examples,the incoming execution instructions may also include specific venuesfields to identify the networked resource or venue to target.

Upon individual receipt, or at a given periodic rate, a given time, whena given number of orders has been received, when all desired orders havebeen received, or when any other desired criteria have been satisfied,processor(s) 104, 1104 can, as a part of parsing or otherwise processinginstructions at 204, sort and/or group the stored records according toany one or more desired criteria, e.g., by type of transaction requestand interest identifier. For example, in the first example scenarioabove:

<buy> <CUSIP No. AA> <18,000> <price C> <res. 7,000> <price G> <source4> <sell> <CUSIP No. AA> <10,000> <price A> <res. 9,000> <price D><source 1> <sell> <CUSIP No. AA> <14,000> <price A> <res. 2,000> <priceE> <source 6> <sell> <CUSIP No. AA> <13,000> <price A> <res. 6,000><price D> <source 3> <sell> <CUSIP No. AA> <21,000> <price A> <res.5,000> <price E> <source 5> <sell> <CUSIP No. AA> <19,000> <price A><res. 3,000> <price D> <source 1> <buy> <CUSIP No. BB> <15,000> <priceC> <res. 7,000> <price F> <source 4> <buy> <CUSIP No. BB> <22,000><price C> <res. 4,000> <price B> <source 4> <buy> <CUSIP No. BB><12,000> <price C> <res. 1,000> <price B> <source 2> <buy> <CUSIP No.BB> <16,000> <price C> <res. 8,000> <price F> <source 5> <sell> <CUSIPNo. BB> <20,000> <price A> <res. 3,000> <price D> <source 1> <sell><CUSIP No. BB> <11,000> <price A> <res. 8,000> <price D> <source 3><sell> <CUSIP No. BB> <17,000> <price A> <res. 6,000> <price H> <source6>As shown, various data fields in the transaction request records can bereordered or otherwise reformatted as needed or desired, to suit theprocessing needs of the routing processor(s) 104, 1104. For example, asshown, the association of a “source” data item associated with orotherwise accorded a different priority, to facilitate efficientordering while permitting the processor(s) 104, 1104 to reportfulfillment of transactions/requests on completion of order processing.

Process 204 can further include aggregation by processor(s) 104, 1104 ofreceived and sorted transaction requests, into collected or consolidatedorder(s) for specific types of transactions in specific interest(s),e.g., by summing total or subtotal quantities associated withcorresponding transaction requests, thus:

<buy> <CUSIP No. AA> <18,000> <price C> <res. 7,000> <price G> <sell><CUSIP No. AA> <77,000> <price A> <res. 18,000> <price D>          <res. 7,000> <price E> <buy> <CUSIP No. BB> <65,000> <price C><res. 15,000> <price E>           <res. 5,000> <price B> <sell> <CUSIPNo. BB> <48,000> <price A> <res. 11,000> <price D>           <res.6,000> <price H>

FIG. 9 shows a process and dataflow diagram of example processes anddataflows for an order router/system 104, 1104. In some examples, theorder router/system 104, 1104 may include a client message processingmodule 2105 whereby processor(s) 104, 1104 and/or other hardwarecomponents can be configured to receive waves of client messages. Insome examples, client order instructions may be received in waves ofmultiple messages. In some such examples, the client message processingmodule may be configured to stage order messages in a buffer or otherorder wave staging memory/storage device 2110 until all order messagespertaining to a particular wave are received.

In one example, a data record to be provided by a source 102, 1102 torequest a transaction in a given interest, on stated terms, can, inaddition to other fields containing transaction request details,include:

<WaveID> <Total number of orders in wave> <Index of current order>  <Timing Mode for Wave> <Timing offset for manual timing   mode><security key(s)>

In another example, instead of including a data field indicating thetotal number of orders in wave, a data record may include a <wavecomplete> flag to indicate that the current order message is the lastmessage in a wave.

In some examples, the incoming order messages can be in a FIX (FinancialInformation eXchange) format, or any other standard or proprietaryformat/protocol.

As illustrated in the example in FIG. 9, once individual orders orcomplete waves have been received, they may be stored in one or morebuffers or other memories/storage devices until they are ready to beprocessed by a wave timing handler 2115. In the example system in FIG.9, the complete order requests (including individual orders and completewave orders) may be stored in multiple levels or queue. For example,order requests may be initially be stored in a client session queueuntil they are selected to be moved to a wave queue. In some examples,the system is configured to only allow N waves to be present in the wavequeue. In some instances, this may prevent a high volume session fromdominating the wave queue and routing resources. In some examples, thesystem can be configured to allow more than N waves into the wave queuewhen other session queues have not used up their allotment.

In other examples, complete order requests may be stored directly intoone or more wave queues (i.e. without any intermediary client sessionqueue).

The wave timing handler 2115 can be implemented by one or more hardwaredevice(s) and/or processor(s) 104, 1104 configured to manage queue(s)and determine which order(s) in the queue(s) will be transmitted to thevenues next.

In some examples, a modified first-in, first-out (FIFO) approach may beused by the wave timing handler. However, rather than a strict FIFO, insome embodiments, the timing handler may be configured to rearrange,cherry-pick, skip, or otherwise select the order(s) in a sequence otherthan simply traversing the queue. For example, in some embodiments, thefirst M wave orders in the queue can be processed to determine asequence for routing those M wave orders, or simply to determine whichof the M wave orders will be routed next irrespective of the subsequentordering or otherwise.

In some examples, the wave timing handler can be configured to determinea sequence in which to route the wave orders to increase the out ofavailable order routing throughput, to decrease the period of timerequired to route all of the orders, and/or to optimize any otheroperational aspect of the router(s) 104, 1104.

In some examples as described herein or otherwise, the ordering of theprocessing of waves can be based on venue latencies, throughputs, marketdata (e.g. available liquidity, pricing, etc.), etc.

When all desired signal sets have been received at 202, and optionallysorted, accumulated, and/or otherwise processed at 204, at 208processor(s) 104, 1104, using instruction sets processed at 204, canprepare execution-request signal sets for transmission toresources/execution processors 106, 1106. Such execution-request signalsets can comprise any necessary or desirable signals for causingrequested processing, including content or data and command signals. Forexample, in embodiments adapted for processing of requests fortransactions in financial interests, requests may be sorted and/oraggregated on the basis of interest(s) to be traded, quantities ofinterest(s) to be traded, price, etc., and associated with suitableexecution command signals. The form of any execution command signalsassociated with a given request can depend, as those skilled in therelevant arts will recognize, on the nature and type of requests to beexecuted and the processors 106, 1106 by which they are to be executed,as well any networks 110, 1110 over which signals exchanged betweenprocessor(s) 104, 1104 and 106, 1106 are to be sent, includingapplicable protocols and instruction formatting requirements. Ergo, datapertaining to any or all of systems 106, 1106, 104, 1104, and 110, 1110,protocols used thereby, and/or information related to interests traded,offered, or described thereby may be accessed and used by processor(s)104, 1104 in parsing and preparing instructions for execution ofprocessing by any of processors or resources 106, 1106. Sources 1126 ofsuch data may include, for example, exchange market data system 1126 v(FIG. 1B) which, for example, in embodiments of the invention adaptedfor processing of financial transactions, can include informationreceived from various exchange systems 1106, news information sourcessuch as Bloomberg or Reuters, and/or other sources.

It is sometimes necessary or desirable, in assembling requests for dataprocessing using networked processing resources, including manyresources configured for use in executing financial transactions, tobreak execution and/or other processing requests into multiple parts.Such parts, or segments, can, for example, correspond to portions oflarger orders or other data processing requests, to be executed by aplurality of networked resources 106 such as exchange servers or otherexecution processor or handlers 1106. For example, if a plurality ofexchange servers or other markets are available for execution of atransaction request representing a purchase order for a significantamount of a financial interest such as a stock or bond, it may benecessary or desirable to split the order into multiple parts, forexecution in multiple markets and/or by multiple exchange servers 1106.For example, sufficient quantities of specific interests may not beavailable, at all or at desirable prices, on a single exchange: in orderto fill an order entirely, it may be necessary or desirable to break asingle order into smaller segments and route it to multiple exchanges.

Thus, for example, in various embodiments of the invention directedtoward the processing of requests for transactions in financialinstruments, when a router 104,1104 is requested by one or more sources106, 1106 to complete a transaction in one or more financial interests,the router 104, 1104 can, in preparing signal set(s) representingrequests for the transactions, access information available from sourcessuch as market data source(s) 1126, as well as any one or more executionprocessor(s) 106, 1106, to determine the quantities of such interestsavailable through the respective processors 106, 1106 and the termsunder which such quantities are available, and can construct anexecution request signal set configured for routing to each of therespective desired processors 1106, 1106, based on the number ofquantities available at the most favorable terms.

For example, continuing the first example above, it may be necessary ordesirable to split one or more incoming processing requests into smallerparts, directed to a plurality of exchanges, in order to obtainfulfillment of the complete order(s). This can be accomplished by, forexample, accessing data representing current order books provided by oneor more of exchange servers 1106 and dividing the order(s)correspondingly, using known data processing techniques. Thus, forexample, the aggregated ‘sell CUSIP No. AA’ order above may be brokendown into portions or segments and associating with data representingsuch segments URLs or other network resource address identifierssuitable for use in routing the various segments to a plurality ofexchange servers A1-C3, as desired, thus:

<exchange A1> <sell><CUSIP No. AA> <15,000> <price A> <res.      6,000><price D> <res. 2,000> <price E> <exchange B2> <sell> <CUSIP No. AA><27,000> <price A> <res.      6,000> <price D> <res. 2,500> <price E><exchange C3> <sell> <CUSIP No. AA> <35,000> <price A> <res.      6,000><price D> <res. 2,500> <price E>

As described herein or otherwise, in some examples, preparing 208execution requests can include combining, or splitting orders into feweror greater orders to a plurality of different venues. In some examples,preparing 208 execution requests can include formatting, rearranging orotherwise preparing original, split and/or combined orders into FIX orother format(s) compatible with the respective destination venues.

In some embodiments, in preparing the execution/trade requests, the SORrouting processor(s) 1104 may be configured to determine which venue anorder should be routed to. Various factors may be considered by the SORrouting processor(s) 1104, such as quote size, cost structure, supportof various order types, rebate structure, average execution speed,latency, latency variance, historical fill rate, etc.

While the example method 200 in FIG. 2 illustrates the preparation ofexecution requests 208 and the determination of timing for executionrequests 210 as separate blocks, in some embodiments, the preparationand determination of timing for requests may be done in opposite orders,concurrently, and/or may be part of the same process. For example,various factors and observed/measured data as described herein orotherwise may be used to both prepare execution requests and todetermine the timing for those requests.

As will be appreciated by those skilled in the relevant arts, executionof individual portions of a distributed transaction or other multi-partdata processing request such as a transaction in financial interestsplaced in multiple exchanges by a plurality of network resources, suchas market or exchanger servers 1106 or other execution processors 106,typically requires different amounts of time. That is, if multiple partsof a desired transaction execution request are sent simultaneously to aplurality of exchange execution processors 106, 1106, each part orsegment of the transaction request may be expected to execute at adifferent point in time. This is because the amount of time, or‘latency,’ required for transmission of execution request signals fromthe order router(s) 104, 1104 to the different various resources orexecution processor 106, 1106 across a network 110, 1110 or othercommunications path; for actual processing of corresponding portions ofthe execution request by the corresponding processors 106, 1106; and/orfor return of confirmatory or other data to the order router(s) 104,1104 typically varies depending upon a number of factors, including forexample the network paths between the router(s) 104, 1104 and executionprocessors 106, 1106; the amount of network traffic being processed bythe network(s) 110, 1110; the number of requests being handled by theindividual execution processors 106, 1106, etc.

For a number of reasons it can be important, in such cases, tosynchronize execution of two or more portions of a multi-part executionrequest. As one example, when an execution request represents a requestfor execution of multiple parts of a financial transaction in multiplemarkets or on multiple exchanges, non-synchronized, staggered executionof individual portions of the transaction by multiple correspondingservers can affect both the possibility of completing later portions ofthe transaction and/or the terms under which such later portions may becompleted.

A particular example of the desirability of synchronizing executionrequests may be illustrated through reference to FIG. 3. In the exampleshown in FIG. 3, system 100, 1000 comprises order router 104, 1104 and aplurality of networked execution resources 106, exchange servers orexecution processors 1106 “Exchange 1,” “Exchange 2,” “Exchange 3.” Inaddition, system 100, 1000 of FIG. 3 further comprises a co-locatedtrading server 304 configured to execute trades or other transactions onexecution resource 1106 “Exchange 1.” As noted in the Figure, co-locatedtrading server 304, which employs a relatively low-latency tradingalgorithm, is associated with Exchange 1 in such manner that it canexecute transactions with Exchange 1 in a relatively short period oftime compared to the amount of time required for other processors, suchas router(s) 104, 1104, to complete similar transactions withExchange 1. For example, co-located server 304 can be communicativelylinked with Exchange 1 by direct wireline connection, or otherrapid-processing system. Moreover, Exchange 1 is capable of completingan execution request with non co-located processor(s) 104, 1104 in arelatively shorter period of time (i.e., with a “lower latency”) than iseither Exchange 2 or Exchange 3. In other words, as shown in FIG. 3,latency Time X<Time Y and Time X<Time Z, while an execution time for atransaction between co-located server 304 and Exchange 1 is less thanany of Time X, Time Y, and Time Z.

If, for example, signals representing a request to trade in one or morefinancial interests is received by a router processor 104, 1104 from oneor more request sources 102, 1102, and the request is of such quantityor magnitude that an order reflecting the request will be too large tobe completely filled by any one of Exchanges 1, 2, or 3, the orderrouter 104, 1104 may attempt to check availabilities on the variousavailable processors 106, 1106 and split the order accordingly, in orderto route a portion of it to each of Exchange 1, Exchange 2, and Exchange3. If the router 104, 1104 of FIG. 3 simultaneously transmits to each ofexecution processors 106, 1106 Exchange 1, Exchange 2, and Exchange 3 adivided portion or segment of the request for execution of the requestedtransaction, it is possible that trading server 304 (which might, forexample, be operated by a high-frequency trading entity, or otherspeculative investor) will be able fill a portion of that transaction onExchange 1 by, for example, acting as a counterparty to the proposedtransaction by selling or buying all or a portion of the transactionrequest forwarded to that exchange by the order router 104, at termsstated in the request for the transaction, and have time in which tochange or otherwise post terms for filling remaining portions of theorder on Exchange 2 and/or Exchange 3, on terms more favorable to theparty making the transaction(s) available (e.g., the party operating oracting through the server 304) than those offering such transactions(e.g., those behind orders provided by request processor(s) 104, 1104)might otherwise have sought. In other words, for example, the co-locatedtrading server 304 may, due to the difference in execution latenciesassociated with trades with Exchange 1, Exchange 2, and Exchange 3, beable fill a portion of the requested transaction on Exchange 1 and moveto improve its terms, by for example raising or lowering its bid/offeredprice, for filling remaining portions of the transaction on Exchange 2or Exchange 3 before such remaining portions can execute atpreviously-stated prices, in order to increase its operators' orbeneficiary(ies) own profits, or the profits of other traders offeringsimilar interests on those Exchanges.

As may be seen in FIG. 3, such possibilities (which can be referred toas ‘latency arbitrage’ opportunities) can exist when:

Time X+Time A<Time Y and/or

Time X+Time B<Time Z

It will be appreciated by those skilled in the relevant arts that, evenwhere transaction or other processing request signals are sentsimultaneously to each of Exchanges 1, 2, 3 from the router(s) 104,1104, the time required for each divided portion of the request to bereceived, acknowledged, and/or processed by the respective resources106, 1106 (e.g., Times X, Y, Z) may in general be different, for exampledue to differences in network communications paths and processing speedsin any or all of processor(s) 104, 1104 and/or 106, 1106. Similarly, thetime required for trading server 304 to change terms of transactionofferings in each of Exchanges 2 and 3 may in general differ.

Among the disadvantages which can arise in such cases is that tradersrepresented by request source(s) 102, 1102 may pay higher prices inexecuting their trades than they otherwise would have, in the absence ofsuch arbitrage opportunities; or, if prices on subsequent exchanges arechanged sufficiently to put them outside terms stated in their executionrequests, they may not be able to complete transactions in desiredquantities—for example, all or part of a transaction routed to anexchange processor 1106 may not trade in view of a changed price.

In such examples, in which a trade instruction may not be fullyfulfilled at an exchange server 1106 due for example to price or otherterm manipulation by a third party taking advantage of latencies, inprosecuting data processing requests in one or more exchange servers1106 it may be useful to time or schedule the sending of trade requeststo multiple exchange servers 1106 such that the execution of such traderequests at all exchange servers 1106 happens in a synchronized manner,such as, for example, in a substantially concurrent manner. Inparticular, it may be useful to synchronize the execution of signalprocessing execution requests, or portions or segments thereof, inmultiple networked computing resources 106, 1106, for example such thatthe signal processes are received, acknowledged, and/or executed by theresources 106, 1106 in a substantially concurrent manner.

In some examples it may not be necessary for the signal processes to beexecuted in each processor 106, 1106 to be executed simultaneously, butmay be sufficient that:

Time Y−Time X<Time A, and/or

Time Z−Time X<Time B,

such that execution of the request(s) or segments thereof occurs beforeany change in terms can be implemented by a trading server 304. The useof such synchronized timings can, for example, cause:

Time X+Time A>Time Y and/or

Time X+Time B>Time Z

and thus, for example, defeat latency arbitrage opportunities. In someembodiments, therefore, the invention provides router(s) 104, 1104 theability to execute transactions across multiple resources 106, 1106 withminimal or no time variance, such that algorithms run by trader(s) 304employing low-latency algorithms are given insufficient time to react tomarket changes.

In some embodiments, the timing range required to ensure that latencyarbitrage as described in the above equation may be minimized and/oreliminated may determine an acceptable range for the routing of signalsrepresenting orders to one or more trading servers 304.

Thus, in these and other cases where synchronization is desired, at 210processor/router 104, 1104 can determine absolute or relative timings tobe assigned to, or otherwise associated with, various portions orsegments of an execution request, in order to obtain the desiredsequencing. Such timings can be determined in order to cause any desiredsynchronization: for example, timings configured to cause simultaneous,or substantially simultaneous, execution can be determined, or timingsconfigured to cause any desired sequencing can be determined.

In some examples, a sequence for routing requests can be defined bydetermining acceptable timing parameters (including ranges) to staggerthe routing of the different routing requests. These sequences caninclude requests to different venues or multiple requests to the samevenue. In some examples, the timing sequence can be determined so as tocause related requests to be executed within a defined time threshold.This time threshold can be based on an observed latency arbitrage timingor otherwise.

Thus at 210, a timing parameter can be determined for each signalprocessing execution request, or portion thereof, to be assigned to eachrespective networked computing resource 106, 1106. The parameters aredetermined in such manner as to cause synchronized execution of thesignal processing execution requests at each of the respective networkedcomputing resources 106, 1106. This determination can be based at leastpartly on a corresponding determined latency in the execution time ofsuch request(s) and/or portion(s), such as for example any or all oflatencies A, B, X, Y, Z of FIG. 3, and/or any other relevant latencies,in the execution of signal exchanges between the router processor(s)104, 1104 and each of the networked computing resources 106, 1106, or inthe processing of other such signals by any of such devices.

In some embodiments, the timing parameters may be assigned to respectivesignal processing execution requests, rather than networked computingresources 106, 1106. In some embodiments, the timing parameters aredefined as ranges for execution, and may be used in accordance toapplied business logic to determine and/or implement routing strategiesfor association with one or more respective signal processing executionrequests. For example, timing parameters may be used to indicate that aparticular set of signal processing execution requests should beprocessed/received/executed within a particular time range, with thetime range being determined dynamically by the SOR router processor(s)1104. For example, a time range may be determined based on variousbusiness logic flows receives as parameters the liquidity on the orderbook of a particular venue. In some embodiments, a time range and/ortiming parameter may also be satisfied through the selection of aparticular routing path or communication link.

In some embodiments, the timing parameters may take into considerationmonitored network conditions and/or characteristics. For example, thevariance, standard deviation, median, mean, mode, kurtosis, etc.

In some embodiments, the timing parameters may be determined based on atiming range and/or probability(ies) that a particular trade will arriveand/or be executed within a particular latency range. As illustrated inFIG. 5, historically monitored latency data can in some examples provideprobability(ies) and/or other statistical data such as standarddeviations, variance, etc. In some embodiments, the SOR routedprocessor(s) 1104 may be configured to determine timing parameters whichfall within defined statistical thresholds.

As an alternative to, or in conjunction with latency required fortransmission, network characteristics may be monitored and utilized bySOR router processor(s) 1104 for the routing of orders.

Monitoring network performance can be a non-trivial task as networkconditions may be constantly changing due to a variety of factors, suchas network congestion, traffic, available bandwidth, channel strength,noise, artifacts, network events, etc., and the performance may affectthe transmission and/or routing of orders. There may be venue-specificnetwork issues, such as congestion at exchange gateways, etc.

In some embodiments, monitored network performance may be approximatedand/or modeled using one or more measurements, for example, to determinestatistical values, such as mean, mode, median, variance, covariance,correlation, kurtosis, skew, standard deviation, etc.

The monitored network performance may be monitored with varying levelsof granularity and scope, for example, an individual network pathway maybe monitored, a venue may be monitored, etc. In some embodiments, theremay be multiple network pathways that may be used to route a signal to avenue, and the pathways themselves may be monitored for its networkcharacteristics.

For example, there may be three different network links, A, B and C, andif A and B are congested, network link C may be selected.

In some embodiments, the monitoring of network performance may beweighted such that more recent network measurements have greatersignificance and/or influence on a model, to reflect the currency of themeasured information.

Network monitoring may be performed based on a variety of data, forexample, scheduled return trip heartbeat messages may be sent toperiodically determine network latencies across a number of links. Insome embodiments, historical order information and/or routinginformation may be utilized in determining network performance. Forexample, the router 1104 sent a dozen orders on this link in the last 2ms, and a number of latencies having particular characteristics wereencountered.

In some embodiments, test messages, duplicate messages, etc., may alsobe transmitted to determine network latency characteristics. Messageshaving various lengths and/or instructions, or a lack of instructionsmay be compared to one another to determine latencies associated withvenue processing.

In some embodiments, network monitoring can be configured to determinedifferent components of a trade latency. For example, when roundtriplatency is monitored, the roundtrip latency may be split between two orthree components. For example, three components can include the latencyassociated with transmission of a request being sent to the venue, thelatency associated with the execution of the request by the venue, andthe latency associated with transmission of a response being sent backfrom the venue. In another example, the roundtrip latency may be splitbetween transmission latencies (i.e. combined measurement of thetransmission latencies for sending the request to the venue, and forreceiving the response from the venue), and the latency associated withthe execution of the request by the venue. Other combinations ofdifferent latency-related components may also be used.

In some examples, the determination of the different components of aroundtrip or other aggregate latency may be based on monitoring theaggregate latency for different request types. For example, requests fordifferent order types or for quotes may result in different aggregateroundtrip latencies and may, in some examples, be used to determinedifferent components of the aggregate latencies, and/or to determinetiming parameters for different request types.

In some examples, the processor(s), at 208 and/or 210, may be configuredto select venues to target and/or adjust timings based on other factorsas described herein or otherwise. In some examples, the processor(s) toselect venues and/or adjust timings based on data associated with avenue. This data can, in some examples, be accessed from a data sourceand/or can be compiled or observed based on capture ratios, latenciesand the like.

In some examples, the processor(s) may be configured to prefer venues orto have longer timing differentials for venues which are less likely tohave more competition for trade requests (e.g. by a co-located and/orhigh-frequency trader). In some examples, the processor(s) may beconfigured to have different preferential rankings or timing adjustmentsfor different venues based on factors which may be indicative of greatercompetition or a greater risk of losing trades to arbitrage. In someexamples, explicit knowledge of an opportunistic co-located trader canbe accessed from a data source or can be implicitly determined based onlower capture ratios and/or lower timing differentials involving theparticular venue.

In some examples, the processor(s) may be configured to have differentpreferential rankings or timing adjustments for venues based on the feeand/or rebate structure of the venue. For example, venues which providerebates for posting certain trades may be more likely to havecompetition from high-frequency traders.

In some examples, when multiple venue options are available, theprocessor(s) may be configured to select venues which are less likely tohave greater competition. In some examples, the processor(s) may beconfigured to generate timings which may risk losing liquidity from thevenue with greater competition (e.g. scheduling it later in the sequenceor with a greater delay) if it increases the chances of capturingliquidity at other venue(s). In some examples, the processor(s) maygenerate tighter timings for waves involving venues with greatercompetition.

In some examples, the processor(s) can be configured to select venuesand/or adjust timing differentials based on the lost liquidity or lostgross value which would be suffered if a trade request were to arrivetoo early or too at the venue. This may be, in some examples, based onposted liquidity, posted bid/ask pricing, etc. The processor(s) may beconfigured to lower the preferential ranking or tighten the timingdifferential for venue(s) which would result in a greater losses(liquidity or value) if a trade request to the venue(s) were to arrivetoo early or late resulting in potential arbitrage losses.

In some embodiments, the SOR router processor(s) 1104 may be configuredto automatically adjust the value of timing parameters and/or the sizeof timing ranges in response to various measured values provided asfeedback, such as a trade capture ratio, a recent fill rate, etc. Forexample, a decreasing trade capture ratio may be indicative ofinformation leakage, and ranges for timing parameters may beautomatically tightened and/or timing parameters modified in response.

For example, in addition to latency and other parameters, the SORprocessor(s) can be configured to monitor the fill rate or capture ratioof previous orders routed along a particular a route or to a particularvenue. In some examples, the processor(s) can be configured to determinefill rate or capture ratio by comparing the total transaction volume forall routed related-transaction requests with available liquidity. Insome examples, the available liquidity can be the total posted liquidityat each of the targeted venues. In some examples, the availableliquidity may be the total posted liquidity at any venue. In someexamples, the available liquidity may be based on the posted liquiditydata available to the SOR processor(s) at the time that the processor(s)prepare execution requests which may include selecting venues to targetand/or splitting a large order request. In some examples, the availableliquidity may be based on the posted liquidity data available to the SORprocessor(s) when the first transaction request in a wave or sequence isrouted.

In some embodiments, available liquidity can include publicly postedliquidity and/or forecasted liquidity. The processor(s) monitoring thedata associated with the networked computing resources can be configuredto monitor whether publicly posted liquidity that is captured by apreviously routed data processing segment is subsequently replenishedwith new publicly posted liquidity and/or the timing between thecapturing of the original liquidity and the re-posting of the newliquidity. The preparation of data processing segments and determinationof their timing parameters can be based on this forecasted liquiditydata.

In another example, the processor(s) may track or identify when multipledata processing segments (unrelated or related to the same data process)target the same posted liquidity. By monitoring data associated withthese data processing segments to see if their fill rates exceed theposted liquidity, the processor(s) can compile this data as capturedun-posted liquidity data.

Monitoring the data associated with networked computing resources caninclude monitoring both the re-posting of liquidity and the capturing ofun-posted liquidity as forecasted liquidity data which can be used bythe processors to prepare data processing segments and their timingparameters to target such forecasted liquidity data.

In some examples, the processor(s) can be configured to determine thefill rate or capture ratio by compiling theconfirmations/acknowledgements or other response messages from thetargeted venues to determine the total transaction volume that wassuccessfully captured/completed after all the trade requests in a wavehave been routed.

In some examples, the processor(s) can be configured to adjust timingparameters and/or timing sequences based on the determined fill rate orcapture ratio. For example, if a capture ratio for a route or venuefalls from its previous, historical and/or typical value, theprocessor(s) may determine that the timing parameters and/or sequences(e.g. based on latencies and/or other factors) are less effective atavoiding any information leakage or arbitrage trades.

In some examples, when a capture ratio falls below a defined thresholdor by more than a threshold amount, the processor(s) can be configuredto adjust timing parameters (e.g. lower or increase time Y or time Z inFIG. 3), adjust timing sequences and/or lower timing threshold(s) fordifferences in execution times at different venues (e.g. lower Time=Aand/or Time=B in FIG. 3).

In some examples, when a capture ratio falls below a defined thresholdor by more than a threshold amount, it may be indicative of a change ina network device such as failing or close to failing link or componentin a router or other switching device. In some examples, theprocessor(s) can be configured to generate an alert or notification thatthere may be a potential network issue and/or equipment problem. Thealert or notification may include audio alerts, visual alerts on adisplay or light source, message(s) (e.g. email, instant messaging,etc.), or any other mechanism for alerting an administrator that theremay be a network issue and/or equipment problem. In some examples, ithas been observed that a dropping capture ratio can provide an earlywarning of an equipment problem or imminent failure before the router orother affected equipment generates its own warning or failurenotification.

In some examples, the defined threshold or threshold amount may beabsolute or relative values. For example, the processor(s) may beconfigured to adjust timing parameter(s), sequence(s) and/orthreshold(s) when the capture ratio falls 2% or falls 2% of its previousor historical value.

In another example, the processor(s) may be configured to adjust timingparameter(s), sequence(s) and/or threshold(s) when the capture ratiofalls outside its typical variance range or varies by a defined numberof standard deviations from its norm (e.g. when the capture ratio is 1-3standard deviations away from the norm).

In some examples, the processor(s) can be configured to determine thevariance range and/or standard deviations by collecting capture ratiosover X time periods and building a Gaussian or other distribution.

Upon determining that a capture ratio for a route/venue or a pair orgroup of routes/venues is below defined threshold or has fallen by adefined threshold, the processor(s) can be configured to automaticallyadjust timing parameter(s), sequence(s) and/or timing differentialthreshold(s) for the associated route(s) and/or venue(s) to try toincrease the capture ratio for future trade request waves or sequences.

In some examples, the processor(s), at 208 and/or 210, may be configuredto select venues to target and/or adjust timings based on other factorsas described herein or otherwise. In some examples, the processor(s) toselect venues and/or adjust timings based on data associated with avenue. This data can, in some examples, be accessed from a data sourceand/or can be compiled or observed based on capture ratios, latenciesand the like.

In some examples, the processor(s) may be configured to prefer venues orto have longer timing differentials for venues which are less likely tohave more competition for trade requests (e.g. by a co-located and/orhigh-frequency trader). In some examples, the processor(s) may beconfigured to have different preferential rankings or timing adjustmentsfor different venues based on factors which may be indicative of greatercompetition or a greater risk of losing trades to arbitrage. In someexamples, explicit knowledge of an opportunistic co-located trader canbe accessed from a data source or can be implicitly determined based onlower capture ratios and/or lower timing differentials involving theparticular venue.

In some examples, the processor(s) may be configured to have differentpreferential rankings or timing adjustments for venues based on the feeand/or rebate structure of the venue. For example, venues which providerebates for posting certain trades may be more likely to havecompetition from high-frequency traders.

In some examples, when multiple venue options are available, theprocessor(s) may be configured to select venues which are less likely tohave greater competition. In some examples, the processor(s) may beconfigured to generate timings which may risk losing liquidity from thevenue with greater competition (e.g. scheduling it later in the sequenceor with a greater delay) if it increases the chances of capturingliquidity at other venue(s). In some examples, the processor(s) maygenerate tighter timings for waves involving venues with greatercompetition.

In some examples, the processor(s) can be configured to select venuesand/or adjust timing differentials based on the lost liquidity or lostgross value which would be suffered if a trade request were to arrivetoo early or too late at the venue. This may be, in some examples, basedon posted liquidity, posted bid/ask pricing, etc. The processor(s) maybe configured to lower the preferential ranking or tighten the timingdifferential for venue(s) which would result in a greater losses(liquidity or value) if a trade request to the venue(s) were to arrivetoo early or late resulting in potential arbitrage losses.

In some embodiments, the router processor 1104 may be configured foroptimizing order flow across multiple routes and for multiple orders.For example, increasing fill rate for a single route through aparticular routing scheme may disadvantage other routes. The routerprocessor 1104 may be configured to balance the order loads acrossvenues, communication links, network links, time, etc. The routerprocessor 1104 may be configured to determine potential latency impactsthat may arise due to congestion caused by the router processor 1104'sscheduled routing, and schedule and/or rearrange and/or associate timingparameters accordingly. In some embodiments, the router processor 1104may be configured to automatically retune and/or shuffle scheduledtransmissions.

In some embodiments, the router processor 1104 may be configured for theapplication of one or more business rules representing business logic.The business rules may cause the skewing and/or weighting of variousfactors used in determining routing characteristics for a particularinstruction set associated with an order. For example, the rules maydetermine how an order is segmented, which venue(s) an order may betransmitted to, the acceptable range of time, etc.

For example, a particular venue having a particular latency may befavored as the venue may have a high trade capture ratio associated withit, and a higher weight may be associated with the routing of signals tothat particular venue.

As another example, a first venue may provide a deeper order book havingmore liquidity than a second venue. While the first venue may have someless desirable network characteristics, it may be weighted more heavilyfor selection given the amount of liquidity that is available.

In some example embodiments, SOR processor(s) can be configured todetermine venues to target as well as the timing parameters for thosevenues by weighing liquidity and latency variance.

In some examples, latency variance may be caused by factors such asnetwork traffic, poor or varying signal quality of networkcommunications, network node problems, congestion or overloading at thevenue (networked execution processor(s)) and the like.

In some examples, latency variance may be caused by intentionallyintroduced delay(s) by a venue or aspects of a venue's communicationnetwork or device(s). For example, a venue may randomly introduce adelay (e.g. a “speed bump”) to order requests of a particular type, orto all orders. This delay will increase the latency for execution of theaffected order request. In some examples, venue(s) may introduce a fixeddelay (e.g. 350 μs) to order requests. In some examples, venue(s) mayintroduce a delay having a randomly-selected length (e.g. between 5-25ms) to order requests.

In some examples, the order requests to which delay(s) are appliedand/or the length of the delay(s) may be randomly selected, may be basedon the order type, may be based on order parameters/options/flags/etc.,and/or any combination thereof.

In some examples, venue(s) may introduce delays having arandomly-selected lengths which are selected from a ranges based ontype/parameter/options/flags/etc. of the order requests. For example,one classification of orders may be subject to random delays between1-10 ms while another classification or orders may be subject to randomdelays between 14-24 ms.

Data associated with previously-routed data processing segments whichmay have been subject to such random delays that are monitored by theprocessor(s) can, in some instances, show a multimodal distributionillustrative of the 1-10 ms range and the 14-24 ms range.

In some examples, the processor(s) can be configured to access definedaspects of intentionally applied delays from one or more database(s) orother data source(s). In some examples, aspects of intentionally applieddelays may be observed by tracking historical orders and theirrespective processing times. Based at least in part on access and/orobserved delay information, in some examples, the processor(s) can beconfigured to determine latency ranges, variances, and other statisticalvalues for the latencies.

Data associated with previously-routed data processing segments whichmay have been subject to randomly introduced delays that are monitoredby the processor(s) can, in some instances, show a multimodaldistribution illustrative of a range of latencies without the randomdelay, and a second range of latencies including the random delay.

In some examples, the processor(s) can be configured to obtain dataassociated with the available liquidity of a proposed trade for eachnetworked computing resource (venue) which may potentially be used.

In some examples, the data associated with available liquidity may bereceived from publicly available sources and may include the number ofshares openly available at a venue. In some examples, the processor(s)can be configured to determine whether there may be more liquidity thanwhat is openly posted (e.g. iceberg or reserve volume). Thisdetermination may be based on data obtained for historical or pastorders placed with the venues for similar financial interests, volumes,times of day, counter-parties and/or any other order data.

At 208 and 210, the processor(s) can be configured to divide a largeproposed trade into smaller data processes representing smaller traderequests to different venues or other networked execution processorsbased on the latency and liquidity data. In some examples, theprocessor(s) can be configured to select venue(s) and volumes based onliquidity data and latency variance.

In some examples, where a venue has a large available liquidity but alarge latency variance, the processor(s) may be configured to balancethe chance of capturing that liquidity with the chance of losingliquidity at other venues due to market moves during the potential rangeof latency periods.

For example, in a hypothetical situation: venue A has 5000 sharesavailable at price X, and has a latency of 100 μs+/−80 μs; and venue Bhas 500 shares available at price X, latency 80 μs+/−1 μs. If the SOR isattempting to capture all 5500 shares, the processor(s) may beconfigured to route a first transaction request to venue A at t=0, and asecond transaction request to venue B 20 μs as later. If the transactionrequests arrive or are processed in accordance with the mean latencytimes, the transactions should capture the available volume at bothvenues at t=100 μs. However, since the latency variance of venue A is solarge, it is possible that the trade at venue A could execute at t=20μs, and before the trade executes at venue B at t=100 μs, the price oravailability of the 500 shares at venue B may have changed. Similarly,if the trade at venue A does not execute until t=180 μs, the order at Bwhich executed at t=100 μs may have triggered the market to change orcancel the order at A before the request is processed at t=180 μs.

In some examples, the processor(s) can be configured to select networkedcomputing resource(s)/venue(s) based on a liquidity to latency varianceratio. When the available liquidity at a venue is high, the processor(s)may be configured to target that liquidity even if it has a high latencyvariance. In some example scenarios, this may involve sacrificing orrisking the loss of liquidity at another venue.

For example, in the above situation, based on the liquidity/latencyvariance ratio or other metric(s) of venue A versus venue B, theprocessor(s) may be configured to determine timing parameters whichcreate a higher probability that the liquidity at venue A will becaptured. For example, assuming that a competing trading system requires50 μs to observe an order at venue B and change the order at venue A,the processor(s) may be configured to determine timing parameters and/ora timing sequence such that an order request is sent to venue A attime=0 and an order request is sent to venue B after time=51 μs. In thisway, if the order at venue A executes at the slowest end of the range at180 μs, and the order at venue B executes at the fastest end of therange at 130 μs (79 μs latency, 51 μs delay), accounting for thecompeting trading system time threshold of 50 μs, the order at venue Awill be able to be filled without any risk of arbitrage from the orderat venue B. As illustrated by this example, the processor(s) may beconfigured to target venue(s) having large latency(ies) if they havelarge available liquidities, even if this may risk losing orders atother venue(s) having smaller available liquidities.

In this example, the processor(s) were configured to take the worst casescenario; however, based on risk tolerances, the processor(s) may beconfigured to select more aggressive timing parameters and sequences.

In some examples, the processor(s) may be configured to select targetvenues based on liquidity and latency variances. In a first scenario, ifa large proposed transaction can be captured by completely avoidingvenue(s) having large latency variance(s), the processor(s) can beconfigured to select and route trade requests to the venue(s) withoutthe large latency variances.

In some examples, the processor(s) may be configured to split the largeproposed transaction between a venue with large latency variance andother venue(s) with smaller latencies.

If a venue has a large latency variance caused by intentional orengineered delays, in some examples, the processor(s) may be configuredto target the liquidity at the venue by sending multiple orders to thesame venue in a timing sequence. For example, if a venue processorapplies a randomly-assigned delay that is large relative to typicalorder execution latencies, in some examples, the SOR processor(s) can beconfigured to detect that a previously sent order has been delayed andmay send one or more subsequent orders in the hope that one of theorders will be processed sooner. In some examples, the processor(s) maybe configured to detect that a previously sent order has been delayed byobserving order confirmations or response messages indicating that thesubsequent orders to the same venue have been processed.

In some examples, multiple orders can be routed to the venue havingrandomly-assigned delays in conjunction with orders to other venues. Insome examples, by sending multiple orders to the venue with randomdelays, there may be a higher probability that at least one of theorders is processed with little or no delay. In some examples, theprocessor(s) can be configured to coordinate the burst orders to thevenue with random delays with orders to other venues. By using timingparameters and/or a timing sequence based on the low range ofintentionally-induced delays for the random-delay venue, and theexecution latencies for the others, the processor(s) may be able tocapture both the liquidity at the random-delay venue as well as theothers. In some examples, this may reduce lost liquidity at the othervenue(s) and/or may increase the chances of capturing at least part ofthe liquidity at the random-delay venue. In some examples, one or moreof these timings and burst orders applied by the processor(s) may resultin a higher capture ratio.

In some examples, burst orders to a random-delay venue may each be forsmaller volumes to reduce the chance of overfilling the initial proposedorder.

In some examples, the processor(s) may be configured to determine thenumber and size of orders in a burst based on the liquidity and range ofdelays applied by the venue. In some examples, the number and size oforders in a burst may be a function of the number of known arbitragesystems, the port capacity of those systems, and/or the throttle rate ofthose systems for the random-delay venue.

In some examples, the processor(s) may be configured to determine thenumber and size of burst orders based on historical burst orderperformance for the particular venue.

In some examples, when a large portion of liquidity is available atother venues, the processor(s) may be configured to send a burst havingfewer orders to a random-delay venue with a timing sequence based on theshort end of the possible delay range. In some instances, this mayprioritize the capture of liquidity at the other venues whilepotentially sacrificing or giving up some of the liquidity at therandom-delay venue.

In some embodiments, the processor(s) may be configured to send multipleorders to a single venue (random-delay or otherwise) to potentiallycapture reserve or iceberg liquidity which does not appear on the book.

For example, based on data associated with prior orders or data receivedfrom data source(s), the processor(s) may determine that reserve volumemay be available in addition to what is visible at a particular venue.

In some examples, the processor(s) may be configured to divide a largeorder into smaller order requests including two or more order requeststo the same venue. In some examples, these two order requests may berouted with timing parameters and/or sequence to capture reserve oriceberg liquidity at the venue.

For example, upon analyzing historical order and/or venue behaviourbased on past transaction data, if the processor(s) determine that aparticular venue may repost available reserve or iceberg liquidity 80 μsafter an initial order is filled, the processor(s) may be configured tosend two orders to the venue 80 μs apart. In some examples, theprocessor(s) can be configured to send the two orders in a sequence withless than 80 μs between them if the processor(s) determine that thesecond order is likely to be filled irrespective of whether the venuehas reposted the underlying liquidity.

In another example scenario, the processor(s) may determine that venue Ahas 500 shares available at price X, and has a latency of 30 μs; andvenue B has 200 shares available at price X, with a latency of 100 μsand likely has underlying reserve/iceberg liquidity.

In some such instances, the processor(s) can be configured to send afirst order to venue B for 200 shares at time=0, a second order to venueB for 200 shares at time=50 μs (so as to not overload venue B, or toallow the reserve liquidity to become available, or otherwise), and athird order for 500 shares to venue A at time=70 μs. In this example,the timing sequence determined by the processor(s) may enable the systemto capture multiple segments of liquidity at venue B while stillcapturing the liquidity at venue A.

In some examples, the processor(s) can be configured to split order(s)and determining timing parameters/sequences to capture multiple portionsof a reserve/iceberg volume at a first venue and liquidity at othervenue(s) before counter-party systems can change the volume or pricingat either the first venue (with the reserve/iceberg volume) or the othervenue(s).

Arbitrage and other problems caused by variations in execution timebetween servers can also be minimized or eliminated by reducing absolutelatencies in transmission and execution of processing requests. Thus thedetermination of timing parameters as described above can be practicedin combination with procedures which also serve to minimize absoluteamounts of time associated with execution and/or reporting of executionrequests by resource(s) 106, 1106.

Information on determined latencies used in determining timingparameters to be associated with the various portions of a multi-partexecution request provided by router(s) 104, 1104 to a plurality ofexecution processors 106, 1106 may include timing information (e.g.,transmission delays, signal propagation delays, serialization delays,queuing delays, and/or other processing delays at the routerprocessor(s) 104, 1104, the networked computing resource 106, 1106,and/or network(s) 110, 1110, 108, 1108). Such information may beprovided by or received from any source(s), and may be stored in andretrieved from one or more data stores 214. Timing data store(s) 214, invarious embodiments, may include databases or other data structuresresiding in memory(ies) 118, 1018 associated with or otherwiseaccessible by router processor(s) 104, 1104. For example, if executionof a portion of an execution request associated with a first networkedcomputing resource 106, 1106 has a longer determined latency than thatassociated with a second networked computing resource 106, 1106 (as forexample in the case of Exchange 1 vs. Exchanges 2 and 3 of FIG. 3)timing for requests associated portions of a transaction request to berouted to these two networked computing resources 106, 1106 may bedetermined such that an execution request, or portion thereof,associated with the first networked computing resource 106 is timed tobe sent earlier than the request associated with the second networkedcomputing resource 106, with the aim of having the requests executed atthe two networked computing resources 106 substantially concurrently, orwithin an effective minimum time A or B associated with possible termmanipulation by a trading server 304.

In some embodiments, one or more algorithms, which may for example use alatency probability model or other predictive model, may be used indetermining timing parameters to be associated with portions ofexecution requests to be routed to various execution processors 106,1106, based on information associated with such communication and/orprocessing delays, or latencies. For example, a rolling average ofhistorical latency data, accumulated over or relevant to any desireddevices, time periods, or other timing considerations may be used topredict an expected latency for execution of a data processing request.

One example of an algorithm suitable for use in determining timingparameters to be associated by router(s) 104, 1104 with portion(s) ofrequests for execution provided by source(s) 102, 1102, where it isdesired to cause concurrent or otherwise synchronized arrival of suchportions or requests at network resources 106, 1106, is based on anaverage latency between transmission of request signals from router(s)104, 1104 and an appropriate timing reference. Such timing reference(s)can for example include start of processing by the correspondingtargeted resource(s) 106, 1106, and/or receipt by routing processor(s)104, 1104 of a confirmation signal generated by the resource(s) 106,1106 on receipt of the request and/or completion of execution of therequest. For example, in some embodiments, it can be advantageous tomeasure latencies between transmission to a given resource 106, 1106 andreceipt by the router(s) 104, 1104 of a confirmation or acknowledgementsignal, or other appropriate response signal 1260, from such resource106, 1106, and to use such measured latency(ies) in determining timingparameter(s) at 210.

Process step 210 may for example be carried out by an applicationexecuted by, or a module of, or otherwise associated with, routingprocessor(s) 104, 1104 such as a capital management entity or module1126 in the case of a financial system 1000. Determination of a timingparameter to be associated with each part or segment of a multi-partexecution request may, for example, include use of an adaptive exchangeround-trip latency (RTL) learning & compensation logic module 1126 c,such as that shown in Figure FIG. 1B. Referring to FIG. 3, such anadaptive exchange RTL learning & compensation logic module 1126 c maydetermine the timing for each signal processing request (e.g., a traderequest) as follows:

-   -   1) For each portion or segment n of an m-part multi-part        processing request X, a time T1 _(x,n) provided by, for example,        a clock associated with the processor(s) 104, 1104 is        time-stamped by processor(s) 104, 1104 at a desired defined        point within the process of parsing or generating the        transaction order(s), or other processing request(s) X, and is        associated with a processing request signal set record(s)        corresponding to each part or segment n of the m-part request X.    -   2) T2 _(x,n) for each portion n of the multi-part request X is        time-stamped by the processor(s) 104, 1104 when the        corresponding n^(th) portion request signal set has been        received at the targeted exchange 106, 1106, and a corresponding        exchange-generated confirmation message has been received by the        requesting routing processor 104, 1104.    -   3) During the course of a trading day (or other data processing        period), process steps 2 and 3 may be repeated, and        corresponding T1 _(x,n) and T2 _(x,n) determined for each        transaction segment routed to a given execution processor 106,        1106.    -   4) For each portion segment n of a subsequent pending multi-part        execution request Y, the determined timing parameter        RTL_(y,n)=Σ(T2 _(x,n)−T1 _(x,n))/Z, where Z is the number of        previously-executed order segments routed to a given execution        processor 106, 1106 used in the calculation.

Where timing data store(s) 214 store a rolling record of past timingparameters (e.g., a plurality of determined timing parameters RTL_(y,n))associated with one or more execution resources 106/exchange server1106, such data may be used to create a rolling histogram, which may beused to predict current or cumulative latency for each resource106/exchange server 1106. Because such predictions are based on acontinuously-changing (“rolling”) record, this process may be referredto as “online learning.” There may be a component (e.g., an exchangelatency histogram memory or processing component, not shown) within theadaptive exchange RTL learning & compensation logic module 1126 cresponsible for this.

An adaptive exchange RTL learning & compensation logic module 1126 c mayuse predicted latencies to determine appropriate timing parameters to beused in transmitting trade (or other data processing) requests tovarious exchange servers 1106 in order to compensate for differences inexecution latencies associated with such exchange servers 1106, in a waythat reduces, controls, minimizes or eliminates differences in timing ofexecution of portions of divided trade requests routed to differentexchange servers 1106, and thus, for example, reduces or eliminatesopportunities for latency arbitrage by opportunistic traders.

Adaptive RTL module(s) 1126 c can use a variety of algorithms indetermining timing parameters suitable for use in synchronizingexecution of multi-part processing requests. For example, such a modulemay use latency values determined for the various exchanges to determinethe extent to which the router(s) 104, 1104 should compensate fordifferent exchange latencies by sending to the various processors 106,1106 their corresponding portions of a request for processing at, forexample, different times. This can minimize delay between completion ofexecution of each portion by, for example, minimizing the difference intime between receipt of each respective portion by its correspondingexecution resource 106, 1106. (In FIG. 3, for example, this would beshown as minimizing differences between times elapsed at Time X, Time Yand Time Z.) Such algorithms can also account for historical differencesin the time required for execution of trade or other processing orderson the various resources 106, 1106, in addition to communication delays.The adaptive RTL module(s) 1126 c may be configured, for example, todetermine timing ranges, to maintain network monitoring models, networkmonitoring logs, etc.

The adaptive RTL module(s) 1126 c may also be configured for theautomatic tuning of timing parameter/sequences depending on information,such as fill rate, trade capture ratio, predicted networkcharacteristics, etc.

Adaptive exchange RTL learning & compensation logic module(s) 1126 c mayadditionally collect information about market conditions prevailing ineach exchange server 1106 (using, for example, sources of data such asexchange market data source(s) 1126 v), wave orders/executions, actuallatencies and target latencies (e.g., as predicted above) when traderequests are sent. There may be a component within the adaptive exchangeRTL learning & compensation logic module 1126 c responsible for this.

One or more timing parameters associated with execution requests to berouted to any one or more of execution processor(s) 106, 1106 can alsobe provided to the corresponding routing processor(s) 104, 1104 (e.g.,to timing data store 214) by, or determined by such processor(s) 104,1104 using related data supplied by, any one or more market data feed(s)or processor(s) 1126 (including e.g., any one or more of processors or(sub)systems 1126 a-1126 g and/or 1126 v), and/or by processor(s) 106,1106 themselves.

In some examples, the processor(s) may be configured to evolve byrandomly introducing small variations to various timing parametersand/or venue selection factors. By measuring the resulting captureratios over time, and re-adjusting the timing parameters and/or venueselection factors, the processor(s) can, in some examples, evolvetowards more optimal timings and/or factor weightings. In some examples,this feedback loop can allow the processor(s) to automatically adjustfor any changes in the network, venues, behavior of competitors and thelike.

In some examples, by considering capture ratios or fill rates, theprocessor(s) may inherently account for any number of underlying factorssuch as network conditions, networked resource conditions, competitorbehavior without any specific understanding of which underlying factorsmay have affected the capture ratio. In some instances, this may resultin a simpler system and/or more efficient system which can adjust forchanges or variations without potentially complex computations of allthe possible underlying factors.

At 212, the various portions of the optionally aggregated and dividedsignal processing execution request(s) are sent to the respectivenetworked computing resources 106 according to timing parameters orsequence(s) determined or otherwise acquired at 210. Thereafter therequest(s), or the various portions thereof, may be executed by therespective execution resources 106, 1106, with subsequent signalcommunications and processing as needed or desired. As will beunderstood by those skilled in the relevant arts, once they have beenmade familiar with this disclosure, once the parameters of a desiredexecution request have been determined by router(s) 104, 1104, signalsrepresenting those parameters may be assembled, using known orspecialized data processing techniques; formatted according to theFinancial Information Exchange (FIX) protocol and/or any other desiredprotocol(s); and transmitted, written or otherwise communicated to thecorresponding execution processor(s) 106, 1106 using known orspecialized signal communications techniques, and executed in accordancewith requested transaction or other data processes.

For example, continuing the example above, timing delays, or parametersX′, Y′, Z′, one or all of which may be equal to zero or any othersuitable time period, may be determined according the disclosure aboveand associated with the order segments generated by processor(s) 1104for purchase of 77,000 bond lots of CUSIP No. AA bonds at price A, with25,000 lots (18,000+7,000) in reserve at prices D and E, respectively,thus:

<delay X′> <exchange A1> <sell> <CUSIP No. AA> <15,000> <price A>     <res. 6,000> <price D> <res. 2,000> <price E> <delay Y′> <exchangeB2> <sell> <CUSIP No. AA> <27,000> <price A>      <res. 6,000> <price D><res. 2,500> <price E> <delay Z′> <exchange C3> <sell> <CUSIP No. AA><35,000> <price A>      <res. 6,000> <price D> <res. 2,500> <price E>

Thereafter, routing processor(s) 104, 1104 can process the transactionsegments by using timing parameters, e.g., delays X′, Y′, Z′, to causethe corresponding transaction segments to be transmitted or otherwiseprovided to the exchanges 106, 1106 A1, B2, C3 for execution accordingto a desired timing sequence, for simultaneous or otherwise-desiredsequential execution.

Following execution of all or as many portions of routed transaction orprocessing segments, routing processor(s) 104, 1104 can receive fromcorresponding execution processor(s) 106, 1106 data confirming orotherwise indicating such execution, and by accessing data recordsstored in associated memory(ies), can allocate execution results to therequesting source(s) 102, 1102.

Reference is now made to FIG. 4, showing an example of a method 300 ofdetermining timing parameters to be used in managing processing of databy multiple networked computing resources 106. In the embodiment shown,method 300 is an iterative method, and each loop of the method 300 isdenoted as N. Method 300 is suitable for implementation using, forexample, any of various embodiments of systems 100, 1000 and componentsthereof, including particularly router processor(s) 104, 1104 and datasource(s) 1126.

At 302, each of a plurality of networked computing resources 106, 1106is monitored, for example by router processor(s) 104, 1104, executionprocessor(s) 106, 1106, external processor(s) 1126, and/or variouscomponents or modules operated by or otherwise associated therewith, forlatencies associated with receipt and/or execution of signal processingexecution requests. This may be carried out, for example, by amonitoring module (e.g., an exchange RTL measurement module 1126 b, suchas for the financial system 1000) in the router processor(s) 104, 1104.Such monitoring may comprise, for example, time stamping outgoingrequests for processing of data, and comparing times of receipt ofconfirmation(s) or results from processing to the correspondingtime-stamped outgoing request. The difference in time between theoutgoing request and the incoming receipt confirmation and/or dataprocessing results can be defined as a data or signal processinglatency, and stored in memory accessible by the router processor(s) 104,1104. By timing differences between outgoing requests and incomingreceipts, confirmations, and/or results, such latencies can be monitoredon a continual, periodic, and/or other dynamic basis.

At 306, at least one timing parameter associated with latency(ies)observed in execution of signal processing requests provided to themonitored resources 106, 1106 by the routing processor(s) 104, 1104 isdetermined. As described herein, such timing parameter(s) may include,for example, latencies due to communication delay, such as transmissiondelays or other signal propagation delays, and/or processing delays,among others. Typically, corresponding timing parameter(s) aredetermined for each of the plurality of networked computing resources106, 1106 to which a transaction order or other data processing request,or a portion thereof, is expected to be sent by routing processor(s)104, 1104.

In various embodiments, such as in various forms of financial systems1000, and depending upon the types of system(s) to be used and desiredprocessing results, such timing parameters may be determined for one-wayand/or round-trip communications between the routing processor(s) 1104operated by or on behalf of a capital management entity and the exchangeserver 1106; that is, from generation of a multi-part transactionrequest by capital management entity's routing processor 1104 to thereceipt of a response, such as confirmation of receipt of a part of alarger trading order and/or confirmation of execution of all or part ofa requested trade, from the execution resource to which the processingrequest was directed. With reference to FIG. 1B, for example, andexplained above, an RTL measurement may include latencies due any or allof transmission of signals within the capital management entity server1104, processing of signals within the capital management entity 1104,transmission of signals between the capital management entity 1104 and anetwork 1110, transmission of signals within the network 1110,transmission of signals between the network 1110 and the targetedexchange server 1106, and processing of signals within the exchangeserver 1106; for both communications sent from the routing processor(s)104, 1104 and responses (e.g., acknowledgement of communication,rejection of a trade request, confirmation of a trade request, etc.)sent from the exchange server 106, 1106. In such embodiments, the timingparameter(s) may be simply the total time for the round-tripcommunication, or a statistical or other mathematical function thereof.

For example, an exchange RTL measurement module 1126 b, such as thatassociated with SOR 1104 shown in FIG. 1B, may determine a timingparameter as follows:

-   -   1) A time-stamp value T1 is associated by the processor(s) 1104        with a new communication M1 (e.g., a trade request) sent to an        exchange server 1106.    -   2) A time-stamp value T2 is associated by the processor(s) 1104        with any response to the request M1 received from the exchange        processor 1106 to which the request M1 was sent. This response        can be any response such as acknowledgement, rejection, whole or        partial fill, etc., and may depend on the nature of the request        represented by M1.    -   3) The RTL associated with the request M1 is calculated as the        difference between T2 and T1. In some embodiments, as noted        above, RTL may be calculated as an average of the time (T2−T1)        for a past number Z (e.g., 30) of processing requests routed to        each of a plurality of targeted exchange processor(s) 1106.

At 308, timing parameter(s) associated with each networked computingresource 106 may be stored in timing data store(s) 214. As describedherein, a timing data store 214, in some examples, may be a database orother data structure residing in a memory associated with or otherwiseaccessible by the router processor(s) 104. Timing parameter(s) stored intiming data store(s) 214 may be employed in processes such as thosedescribed above in connection with process block 210 of FIG. 2.

Timing parameter(s) determined by processor(s) 104, 1104 may for examplerepresent rolling histogram(s) representing latencies associated withindividual execution processors 106, 1106 and/or other components ofsystem(s) 100, 1000.

FIG. 5 shows an example of a histogram illustrating stored datarepresenting processing latency time values associated communicationsand/or other processing associated with an execution processor 106, 1106in a system 100, 1000. In the example shown, round-trip latency times(in ms) are stored for the most recent 30 transaction requests or othercommunications with a given execution server 106. Although the exampleshows 30 latency times being stored, the number of stored timingparameter(s) used in determining RTLs or other timing parameters may begreater or fewer, and may vary according to conditions such as the timeof day, the season, etc. The results of calculations based on the storedlatencies, and other related data, may also be stored in timing datastore(s) 214. For example, in the example of FIG. 5, in addition to rawlatency times, a rolling average or a rolling mode of the past 30 (orother suitable number) latency times associated with communicationsand/or other processing with or by each execution server 106 may also becalculated and stored in timing data store(s) 214.

As will be readily understood by those skilled in the relevant arts,further factors, including for example desired fix offsets or delays, orscaling factors associated with time of day, day of week, season ofyear, etc., known trading or other data processing patterns, economicconditions, etc., may be used at 210 in determining timing parameters.

Timing parameters determined at 210 can be used by routing processor(s)104, 1104 to synchronize execution of processing requests originated bysource(s) 102, 1102 and directed to processor(s) 106, 1106 by, forexample, associating with such requests, or portions of them to beforwarded for execution by each of multiple processor(s) 106, 1106, dataitems useable by the processor(s) 104, 1104 to cause communication ofthe requests to the corresponding processor(s) 106, 1106 at desiredabsolute or relative times, to achieve desired synchronization of thearrival of the requests at the corresponding execution processor(s) 106,1106. For example, by using data items configured to cause communicationof one or more portions of the requests at given time(s) according to aclock associated with the processor(s) 104, 1104, the processor(s) 104,1104 can cause the request(s) or request portion(s) to be communicatedat a desired time of day, or in any desired relative order or sequencewithout regard to the actual time of day, but rather with respect toeach other or some third index.

At 310, N is incremented by one, or other suitable value, or control isotherwise returned to 302 so that the process 302-308 continues.Optionally process 302-310 continues until a maximum desired number ofiterations has been completed, or until all requests for transactions orother processing by orders have been processed (e.g., routed toexecution processors 106, 1106), or until other suitable criteria hasbeen met.

To aid operators and users of system(s) 100, 1000, or componentsthereof, understand or evaluate the effect of the disclosed method andsystem for causing processing of data by multiple networked computingresources, in some aspects, the present disclosure also provides variousmetrics (e.g., trading benchmarks, in the case of a financial system1000) which may be determined by, and through the use of data generatedfrom, any or all of the various components of a system 100, 1000.

Reference is now made to FIG. 6, which shows comparisons of results oftransmission of multi-part trade execution requests to pluralities ofnetworked computing resources, or execution processors 106, 1106according to an example of the disclosed method and system, to resultsof conventionally-transmitted multi-part trade requests.

FIG. 6A shows results of execution of a multi-part transaction requestusing the disclosed methods and systems to obtain synchronized (in theillustrated case, substantially simultaneous) execution of the variousparts or segments 624 of the multi-part transaction request (a sellorder) by a plurality of exchange servers 106, 1106. In the exampleshown, a fill rate of 94% of an original aggregated order was achievedat the original offer price 630 of $4.21 (shown as “Level 1”). In asecond round of transactions (which was filled in a single transaction,as shown at 626) the remaining volume was sold at a less-desired butstill acceptable price 632 of $4.20 (shown as “Level 2”). The costassociated with the orders filled below the requested order price (i.e.,those orders in Level 2) was $53,000 for the trader systems 1102 (e.g.,client systems) and $10,049 for the capital management entity 1106.

In FIG. 6B, using prior-art trading methods and systems, anunsynchronized multi-part trade request (multi-exchange sell order)consisting of multiple, unsynchronized order segments 624′ for the sameoverall transaction request resulted in an initial fill rate of 47% atthe preferred order price 630 of $4.21 (shown as “Level 1”). A further43% of the request was subsequently filled at the less-desirable price632 of $4.20 (shown as “Level 2”), with the remainder being filled at afurther reduced price 634 of $4.19 (shown as “Level 3”).

Using methods and systems in accordance with the disclosure, avolume-weighted average sale price (VWAP) 636 of $4.2094/share wasrealized, as shown at 628. Using prior-art methods and systems, a VWAP638 of $4.2038/share was realized.

As will be readily understood by those skilled in the relevant arts,systems 100, 1000 can comprise devices or components suitable forproviding a wide variety of further metrics and functionalities. Forexample, reference is now made to FIG. 7, which illustrates two examplesof the provision by a routing processor 104, 1104 or other processor ofa benchmark comparison relative to a market average price provided by,for example, a market news service or other market data source 1126 v.At 646, performance of a system 100, 1000 in synchronized processing ofa multi-part transaction request in accordance with the invention iscompared to a market performance indicator “Average Price Benchmark.”Such average price benchmark, or other benchmark or metric factor, canbe obtained from, for example, any or all of components 1126, 1106, etc.At 644, performance of a system 100, 1000 in un-synchronized processingof a multi-part transaction request in accordance with prior art methodsis compared to the same market performance indicator “Average PriceBenchmark.” Comparison of comparisons 646, 644 indicates that processingof transactions in accordance with the invention provides better resultsfor a seller of financial interests. As will be understood by thoseskilled in the relevant arts, a wide variety of benchmarks may be usedin assessing performance of systems and methods according to theinvention. Such benchmarks may be determined at least partially by thenature of the system 100, 1000 used, and the types of transactions orother execution requests processed by such system.

FIG. 8 is a flowchart illustrating aspects of an example method 800 forcoordinating processing of data by multiple networked computingresources. The method is suitable for implementation by routerprocessor(s) 104 such as, for example, an SOR 1104 or by any one or moreprocessors in system 1000.

Aspects of the example method 800 in FIG. 8 are similar or the same asthose appearing in FIGS. 2 and 4 and described in various exampleembodiments described herein. Therefore, any examples or implementationdetails described with respect to those figures or as described hereincan be applied to the method of FIG. 8. Similarly, examples andimplementation details described with response to FIG. 8 can besimilarly applied to the example methods of FIGS. 2 and 4. All of thesevariants and combinations of these aspects are contemplated by thepresent description.

At 810, one or more processors 104 in the system 1000 monitor dataassociated with the networked computing resources 106. As describedherein, in some embodiments, the data associated with the networkedcomputing resources can, among other things, include data associatedwith data processing segments previously routed to the networkedcomputing resources.

In some embodiments, data associated with previously routed dataprocessing segments can include timing information associated with whena data processing segment is routed (for example, using a timestamp),when a data processing segment is processed at a networked computingresource (for example, a timestamp or processing time field in aresponse message), when a response message is received at the system(for example, using a timestamp). In some embodiments, monitoring suchdata can include determining execution latencies for the data processingsegments (and/or the different latency components) and associating themwith the corresponding networked computing resource.

In some embodiments, monitoring data can include monitoring andcomparing latencies for different types of data processing segments. Forexample, different types of data processing segments routed to the samevenue may have similar transmission latencies, but may have differentlatencies associated with the actual execution of the data processingsegment. In another example, data processing segments having differentlengths or even a lack of instruction may have different latencies whenrouted to the same networked computing resource.

In another example, an improperly formatted data processing segment maybe quickly rejected by a networked computing resource such that theprocessors can be configured to assume that all or most of the latencyassociated with such a data processing segment is attributable totransmission latencies.

In some embodiments, the processors in the system are configured todetermine the different latency components associated with a particularnetworked computing resource based on differences between the totallatencies for different order types/lengths/etc.

In some examples, latency components can include but are not necessarilylimited to outgoing transmission latency, execution latency and returntransmission latency (e.g. of a response message).

Monitored data associated with previously routed data processingsegments can, in some examples, include data in one or more responsemessages indicating whether the data processing segment was successfullyprocesses and/or to what extent it was successful. For example, a dataprocessing segment representing a trade request, data in one or moreresponse messages to the trade request can indicate whether the tradewas filled, how much of the request was filled, and/or a price at whichthe request was filled.

Monitored data associated with the previously routed data processingsegments can include data indicating the liquidity of a financialinterest posted or otherwise known to be available at the networkedcomputer resources at the time an initial data processing segment in awave is routed, or at a time before the initial routing.

In some embodiments, monitoring the data associated with previouslyrouted data processing segments can include determining one or morecapture ratios for each data processing segment and/or for one or moredata processes as a whole. Processor(s) can determine a capture ratio bycomparing the available liquidity targeted at a networked computingresource with the actual liquidity captured by the trade request. Thecapture ratio of previously routed data processing segments can bemonitored on a segment by segment basis and across all data processingsegments divided from the original one or more data processes.

In some embodiments, the monitored data can include data parametersidentifying a risk of information leakage for each of the networkedcomputing resources. For example, parameters can be set to indicate thepresence of a co-located or active low-latency third party server, arebate scheme which encourages one or more trade request types, dataassociated with data processing segments previously routed to acomputing networked computing resource indicating that one or morerelated data processing segments were unsuccessful, etc. In someexamples, monitoring the data includes generating a risk of informationleakage score based on the monitored data.

In some embodiments, the system has multiple network routes to anetworked computing resource. In these embodiments, the monitored dataassociated with a particular networked computing resource may bemonitored/associated on a route-by-route basis.

In addition to routes, in some examples, the monitored data can alsoinclude latency data and or status information for route segment(s)along one or more routes and/or device(s) on one or more routes.

In some embodiments, monitoring the data can include acquiring,measuring or requesting data from one or more components or devices inthe system as described herein or otherwise. Some data monitoring mayinvolve various calculations.

Monitoring data can include storing the data in one or more memories ordata storage devices in the system. In some embodiments, multiple datapoints associated with networked computing resources can be collected toprovide a range and/or distribution/probability for the data.

At 820, the processor(s) receive, from one or more data sources 102,signals representing instructions for execution of one or more dataprocesses that are executable by one or more of the networked computingresources 106. As discussed above, for example with reference to FIG. 2,the one or more data processes can, in some embodiments, representrequests for execution of trades and/or other transactions in financialinterests.

In some examples, the instructions for the execution of data process(es)may include specific parameters for executing the data processincluding: process priority, tolerance value for information leakage, amaximum allowable latency period between any data processing segments, adesired routing path, a desired venue, specific routing instructions,etc.

At 830, based on the monitored data, the processor(s) prepare dataprocessing segments for the one or more data processes. In someexamples, preparing the data processing segments can include combining,dividing, or otherwise determining and preparing a number and size ofdata processing segments for executing the received one or more dataprocesses.

In some embodiments, the processor(s)′ preparation or division of thedata processes into the at least one data processing segments includesselecting to which of the networked computing resources to route one ormore data processing segments. This selection is based on the monitoreddata associated with the networked computing resources.

Preparing/dividing the data processes also includes determining a sizeof each of the data processing segments. In some embodiments, this mayinclude a size of a processing task, or a size of a trade request. Theprocessors are configured to determine the size of each of the dataprocessing segments based on the monitored data.

The selection and size determinations, in some embodiments, can be basedon monitored data including one or more of: available liquidity at thenetworked computing resources of financial interest(s) associated withthe data process, a risk of information leakage, and latency(ies)associated with the networked computing resources.

While the preparation of data processing segments may be based ontargeting available liquidity at multiple networked computing resources,in some examples, the processor(s) may determine that a single dataprocessing segment is to be sent to a single networked computingresource. Alternatively, this may be viewed as sending a size 0 or nodata processing segment to other networked computing resources.

At 840, the processor(s) determine timing parameters for the dataprocessing segments. The timing parameters are based on the monitoreddata, and can identify differences in the timing of initiating therouting of the data processing segments. In some examples, the timingparameters can define timing offsets or delays between which a firstdata processing segment is to be routed relative to the routing time ofa second data processing segment. In some examples, the timingparameters can define a range of times within which a first dataprocessing segment is to be routed relative to a routing time of asecond data processing segment.

In some embodiments, the timing parameters can define a probability orrisk factor for information leakage associated with different timesub-ranges within the range of times.

The timing parameters are determined to as to cause synchronizedexecution of the data processing segments. In some examples, the timingparameters can be determined with the aim of having the data processingsegments execute as closely together as possible. In some examples, thetiming parameters can be determined with the aim of having the dataprocessing segments executing in a desired sequence and/or with desiredrelative timings. In some examples, the timing parameters can bedetermined with the aim of capturing as much liquidity as possibleand/or at as desirable a price as possible. In some examples, the timingparameters can be determined based on the distribution of arandomly-introduced delay at one or more of the networked computingresources.

At 850, the processor(s) initiate the routing of the data processingsegments to their respective networking computing resources based on thetiming parameters.

As described herein or otherwise, the division of data processes, thedetermination of the number, size and destination networked computingresources of data processing segments is based on the monitored dataassociated with the networked computing resources.

In some situations, the processors are configured to consider differenttradeoffs between available liquidity, risk of information leakageand/or latency variance for one or more networked computing resources.

For example, by default or based on a risk tolerance value associatedwith a data source from which a data process has been received, theprocessor(s) can prepare data processing segments and timing parametersto target as much liquidity as possible despite a higher risk of dataleakage and of losing part of the liquidity or a better price.Conversely, based on a different default or lower risk tolerance valueassociated with a data source, the processor(s) can prepare dataprocessing segments and timing parameters to target a smaller amount ofliquidity with a lower risk of data leakage.

As described herein or otherwise, in some embodiments the processors canprepare data processing segments and timing parameters based onavailable liquidity,

As illustrated for example in FIG. 4, by continuously and/orperiodically monitoring the data associated with networked computingresources, the system can, in some instances, establish more accurateand/or up-to-date data such as latencies, risk factors, liquidity, etc.In some examples, over time, the monitored data can establishfrequencies, distributions, etc. of one or more types of monitored data.

In some examples, by monitoring timing parameters and result data forpreviously-filed data processing segments and waves, the processors mayestablish local minima or maxima values that provide better outcomes. Insome embodiments, when determining and/or applying timing parameters forrouting, the processor(s) may introduce random timing variations into atiming sequence. In some examples, these variations may be smallrelative to the timing parameters and/or may be selected such that thevaried times still fall within a range that satisfy the timingparameters. In some examples, the introduction of the timing variationsand by monitoring the data associated with the affected data processingsegments, resulting adjustments to the timing parameters may morequickly converge to a more optimal value, or may discover differentlocal minima/maxima.

In some instances, for example when a particular networked computingresource randomly introduces delays or speed bumps, timing parameterdistributions may be multimodal. In some embodiments, the processors maydivide data processes into at least two data processing segments to berouted to the particular networked computing resource, and can establishtiming parameters based on the multimodal distribution. For example, theprocessors may generate a burst of data processing segments with smallertrade requests with the objective of having at least a subset of thedata processing segments executed without being subject to theintentional delay or speed bump at the networked computing resource.

In the embodiment shown in FIG. 1B, source(s) 1126 of monitored datauseable by processor(s) 104 in preparing financial transaction or otherdata processing execution requests includes a plurality of modules 1126a-g useful in preparing a multi-part execution request. In the exampleshown, modules 1126 a-g include market data processing module 1126 a,exchange round-trip latency measurement module 1126 b, adaptive exchangeround-trip latency (RTL) learning & compensation logic module 1126 c,smart sweeping share allocation logic module 1126 d, smart posting logicmodule 1126 e, regional & national exchange access logic module 1126 f,and aggressiveness management module 1126 g.

Market data processing module 1126 a receives and processes market data,which may be the same as or different from data provided throughexchange market data module 1126 v of the exchange server 1106. Sourcesof such data may be internal to the system 1104, or external, as neededor desired, and may include any suitable private or publicly-availablesources of data useful in preparing execution requests, and particularlysuch requests that are useful in dividing or otherwise preparing atransaction order: information provided can, for example, include thenumbers or quantities and/or prices available on any particularexchanges; historical trading volumes or prices; current and historicaldepth of market(s) or liquidity; reserve sizes; absolute, relative,and/or average price spreads; and stock- or interest-specificheuristics; and/or trends in any or all thereof.

Exchange RTL measurement module 1126 b determines timing parameters foruse in synchronizing execution of multi-part trade or other dataprocessing requests by pluralities of exchange server 1106 s, as forexample explained herein, using statically-defined latency datarepresenting time(s) elapsed between sending of requests or other datato, and receipt of confirmation or execution results from, individualexecution processor(s) 106, 1106.

Adaptive Exchange RTL measurement module 1126 c determines timingparameters for use in synchronizing execution of multi-part trade orother data processing requests by pluralities of exchange server 1106 s,as for example explained herein, using dynamically-defined (“rolling”)latency data representing times elapsed between sending of multipleprocessing requests, or other data, to, and receipt of confirmation orexecution results from, individual execution processor(s) 106, 1106.Histograms and other data models and/or structures representing suchrolling data may be used by module(s) 1126 c in determining timingparameters according to such processes.

Smart sweeping share allocation logic module 1126 d includes astatistical model for strategically oversizing transaction requests,and/or associating reserve quantity(ies) with publicly-posted orders,based on historically observed market data. This module 1126 ddetermines, for example, a suitable oversizing (i.e., over-ordering on atrade request) to be incorporated in an open order, taking intoconsideration predicted hidden reserve quantity(ies) in an exchangeserver 1106, based on statistical data about the hidden reserveavailable in that exchange server 1106 over a given period or underother specified conditions (e.g., the past 30 trade requests). Based onsuch predicted hidden market reserves, a suitably-sized hidden reservecan be determined, and associated with a transaction order, to result ina strategic oversizing of the publicly-viewable order and help to ensurethat an actual desired trading volume is realized.

Smart posting logic module 1126 e includes a statistical model fordetermining the probability of fills (i.e., percentage satisfaction of atrade request) expected to be realized in trade requests routed toindividual exchange servers 1106. Such statistical models may forexample include historical fill data realized on such individualexchanges over a given period (e.g., the past 30 trade requests, lastmonth, previous 12 months, etc.). A smart posting logic module 1126 emay take into consideration factors including, for example, the depth ofthe top of book at each exchange server 1106, the volatility levelacross exchange servers 1106 and the mean latency time to execution of atrade request, among other factors.

Smart posting logic module 1126 e may also be configured for determiningtrade capture ratios for historical orders, for example, by dividing thedesired liquidity at the time the order was submitted and/ortransmitted, with the actual captured liquidity that was achievedthrough the execution of the order.

Regional & national exchange access logic module 1126 f providesinformation about how a trade request should be routed to an exchangeserver 1106, depending on whether the exchange server 1106 is regionalor national. Internally- and/or externally-stored data related tosuitable protocol(s) to be employed, regulations to be observed, etc.,may be employed in providing such data. Such data may be used, forexample, in ensuring that trade or other processing requests forwardedto external resources 106, 1106 by routing processor(s) 104, 1104 aresuitably formatted, in view of the resource(s) 106, 1106 to which therequest(s) are provided, and in ensuring that such request(s) complywith all applicable legal standards.

Aggressiveness management logic module 1126 g includes a probabilitymodel for determining the probability of a fill percentage forindividual exchange servers 1106, and modifying execution requestsrouted to such servers accordingly. Such a module 1126 g may take intoconsideration factors such as, for example, the fill rate at eachexchange server 1106, the depth of book at each exchange server 1106,and the volatility levels across exchange servers 1106, among otherfactors.

In some embodiments, specially constructed order messages may beutilized for order routing. For example, the router processor 1104 maybe for FIX message (or other protocol) message handling. The venue sideof the system, performs the scheduling part of the processing and thefollowing message handling functions:

Adjusting the sequence number—since the sequence number can only bedetermined once the order is scheduled. To do this, the FIX engine willalways generate a null, fixed length sequence number field (like 8 or 9zeros “000000000”), then the scheduler will overwrite this field withthe actual sequence number.

Adjust the checksum based on the sequence number—to reduce theprocessing, the FIX engine will pass a message with a correct checksumfor the “000000000” sequence number field. Then when the schedulerupdates the sequence number, it subtracts the non-zero characters in thesequence number from the checksum; thereby avoiding recalculating thechecksum for the entire message and reducing the processing to a verysimply and mechanical operation.

The sequence number will be passed back to the sending FIX enginewith-in the router processor 1104 so that it can be incorporated intothe session logs/state.

Messages from the venues will bypass the scheduler and be handleddirectly by the FIX engines.

In some embodiments, scheduling and message updating can be reduced tovery “mechanical” operations that lend themselves to high speed codingtechniques and potential hardware acceleration (in FPGA, etc.).

As described above, in some embodiments, the sequence in which orderwaves are removed from the queue and routing to the associated venuescan be based on any of the factors described herein or otherwise.

Routing trade requests in a timing sequence may include delaying one ormore trade requests in a wave/sequence. In some instances, this mayimprove the capture ratio of a trade, but routing a number of traderequests in a timed sequence may inherently take longer than sendingthem all at once.

In some examples, at 208 and/or 210, the processor(s) can be configuredto determine a sequence for routing the trade requests of multiplewaves. In some examples, this may include interspersing, overlapping orotherwise routing at least some trade requests for different wavesconcurrently.

In some examples, the processor(s) can be configured for sequencing thetrade requests of multiple waves while respecting intra-wave timingparameters as well as general timing parameters such as a minimummessage interval that must be respected to avoid overloading or queuingat a venue.

In some examples, general or venue specific timing parameters may bebased on observed timing factors such as latencies and fill rates. Insome examples, general or venue specific timing parameters may be basedon characteristics associated with network devices, links and/or venues.These characteristics can include but are not limited to device, linkand/or throughputs or performance limitations, performance factors basedon service level agreements.

For example, the table in FIG. 10 illustrates example monitored datareceived or determined from monitoring network performance, historicalorders and/or historical order data.

With these example latencies and throughputs, in an example scenario,the 4 order waves illustrated in FIG. 11A are received (and theassociated default timing parameters are displayed).

Without modification, and assuming a 10 microsecond processing ratebetween waves, the table in FIG. 11B lists the times when orders arerouted and in parentheses, the times when orders hit the venueprocessor(s) and the resulting timing error.

In this example, if timing errors must be within 100 microseconds of thetarget, each of waves B, C and D would not meet the target. Thisscenario requires 2130 microseconds of the router's time and 2910microseconds including the venues.

In another example scenario, waves C and D can be reordered to be sentearlier because they are for unrelated symbols. The table in FIG. 11Cshows the timings if wave D is started immediately after wave A, andthen waves B and C are started simultaneously.

While waves B, C and D all miss the 100 microsecond target, there is animprovement to the overall time to handle all 4 waves (1310 microsecondsat the router and 2600 microseconds including the venues).

In the next scenario illustrated in FIG. 11D, the waves are routedanticipating venue congestion and preserving order wave sequences.

As illustrated, all waves meet the 100 microsecond target, but thehandling time has increased to 3700 microseconds.

As illustrated in FIG. 11E, in the next scenario, the wave timinghandler can be configured to schedule orders to be routed in aninter-wave timing sequence, as well as an intra-wave timing sequencebased on both venue throughput/congestion and latencies.

In the above scenario, all waves meet the 100 microsecond target, andthe handling time is 2300 microseconds for the router and the venues.

In some examples, the wave timing handler may be configured to determinean optimal or best available schedule for the next N waves. In someexamples, when N is small (e.g. less than 10), the wave timing handlermay use a brute force algorithm by calculating the resulting handlingtime all possible sequence and selecting the best one.

In some examples, when N is larger or when a brute force approachresults in inordinate delays to the wave routing process, the wavetiming handler may be configured to calculate as many possible sequencesin a defined/allotted amount of computation time/cycles and selectingthe best one.

In some examples, the processor(s) can be configured to apply other jobscheduling optimization algorithms to schedule the different waves inthe shortest handling time.

In some examples, the number of waves to be included in the schedulingalgorithm may depend on the number of venues and/or trade requests inthe waves.

FIG. 12 is a flowchart showing aspects of an example method 1200 forcoordinating processing of data by multiple networked computingresources. At 1210, processors obtain data processing waves from a waveor session queue. The data processing waves identify one or more dataprocessing segments and corresponding networked computing resource towhich each of the data processing segments are to be routed. The dataprocessing waves also identify a timing sequence in which the one ormore data processing segments are to be routed.

At 1220, the processors obtain minimum handling intervals for each ofthe networked computing resources. In some embodiments, the handlingintervals can be based on the monitored data associated with thenetworked computing resources.

At 1230, the processors schedule an order for routing the dataprocessing waves based on the wave timing sequences and the handlingintervals for the networked computing resources. In some embodiments,scheduling the order comprises determining an order which interspersesthe data processing segments of different data processing waves withoutviolating the networked computing resource handling intervals andwithout violating the timing sequences of the data processing waves.

In some examples, the timing sequences can include acceptable timeranges within which one data processing segment can be routed relativeto the time for routing a second data processing segment. In someembodiments, scheduling an order for routing data processing waves caninclude adjusting a routing time sequence for one or more wave withinthese time ranges.

In some embodiments, the processors are configured to obtain M dataprocessing waves from a wave queue, and to determine a total handlingtime for each possible arrangement of the M data processing waves. Theprocessors then schedule the order based on the arrangement having theshortest total handling time.

In some embodiments, the processors consider additional parameters. Forexample, when a choice is available, the processor can be configured toselect an arrangement which uses the fewest number of networkedcomputing resource (venue) sessions. In another example, the processorscan consider whether different data processing segments are traderequests are for the same financial interest, and may select anarrangement to avoid or reduce any conflicts. Other similar factorsinclude order sizes, prices, parties to a trade, etc.

The selection of the number of data processing waves M may be dependenton the amount of time required to evaluate all the order arrangementsfor the M waves versus the total handling time required by the orderwave processor to route a wave. If the evaluation time is too high, itcan become a bottleneck and waste routing resources by allowing them toidle while the evaluation is being performed. This also can increase theamount of time during which the prices or liquidity at the networkedcomputing resources may change thereby potentially reducing captureratios. If M is too small, routing resources may be waste because theremay be less opportunity to optimizing the scheduling of the waves.

In some examples, the number of waves M can be selected dynamically asresources and/or wave handling times change.

In some embodiments, the processors compute as many of the possiblearrangements for the M waves that can be evaluated before a time limitexpires. Once the time limit expires, the best schedule from theevaluated arrangements is selected.

In some embodiments, the processors are configured to select andschedule M waves from the wave queue. However, only the first W wavesfrom the scheduled order are routed while the remaining M-W waves in theschedule and the next W waves in the wave queue are evaluated todetermine a next scheduling order.

In some embodiments, the system route one scheduled order of waves (or asubset of the order) while concurrent computing the schedule for thenext order. This pipelining of the process can, in some examples, leadto improved performance.

In some examples, the selection of waves from the wave queue and/orsession queues may be based on one or more priority schemes. Theseschemes can include batches, round robins, first-in first one, etc. Insome embodiments, the priority scheme is based on meeting regulatoryrequirements. In some examples, the priority scheme includes rules basedon financial interest symbols, order sizes, prices, priorities, clients,pricing schemes, contractual obligations, etc.

In some embodiments, the selection of waves from a queue may be based onthe total routing/handling time for the particular wave. For example, ifa wave has tight timing parameters (i.e. small delays between dataprocessing segments, and therefore a shorter handling time), it may beprioritized over a wave having a longer handling time if it can easilyfit in a next schedule or otherwise result in higher throughput.

At 850, the system routes each of the data processing segments in theordered data processing waves based on the schedule.

As described herein, the processors monitor data associated with thenetworked computing resources. In some embodiments, the processorsobtain a minimum handling interval time by identifying with themonitored data when two or more data processing segments routed to thesame networked computing resource resulted in one or more latencieswhich are longer than a historical latency value for the networkedcomputing resource. The difference in time between the involved dataprocessing segments can be used to define a minimum handling time forthe networked computing resource. In some examples, the longer latencymay be indicative that one of the data processing segments was queued atthe networked computing resource (or along the path).

Similarly, in some embodiments, the processors can identify a number ofdata processing segments that can be typically routed to the samenetworked computing resource within a minimum handling time before alonger than typical latency is observed. In some examples, this numbermay be indicative with a number of processors, etc. at a networkedcomputing resource that must be busy before a subsequent request isqueued.

In some embodiments, when the monitored data (e.g. latency, captureratio) indicates that a networked computing resource (or route) iscongested, the processors can delay, reschedule or re-prepare any wavethat has at least one data processing segment for routing to theaffected networked computing resource.

In some embodiments, monitoring the data includes identifying in variousdata processing segments from the scheduled wave orders which are to berouted to the same destination or otherwise have the potential to act asa good test case to determine timing parameters, handling times,congestion thresholds, etc. In some examples, by identifying andspecifically monitoring these data processing segments with testcapabilities, the system may, in some instances reduce the need to sendtest data processing segments.

While the disclosure has been provided and illustrated in connectionwith specific, presently-preferred embodiments, many variations andmodifications may be made without departing from the spirit and scope ofthe invention(s) disclosed herein. The disclosure and invention(s) aretherefore not to be limited to the exact components or details ofmethodology or construction set forth above. Except to the extentnecessary or inherent in the processes themselves, no particular orderto steps or stages of methods or processes described in this disclosure,including the Figures, is intended or implied. In many cases the orderof process steps may be varied without changing the purpose, effect, orimport of the methods described. The scope of the claims is to bedefined solely by the appended claims, giving due consideration to thedoctrine of equivalents and related doctrines.

What is claimed is:
 1. A system for coordinating processing of data bymultiple networked computing resources, the system comprising at leastone processor configured to: receive from one or more data sourcessignals representing instructions for execution of a plurality of dataprocesses executable by a plurality of networked computing resources,the data processes representing a proposed trade in a financialinterest; obtaining data associated with available liquidity of thefinancial interest at each of the plurality of networked computingresources; divide each of the plurality of data processes into aplurality of data processing segments, each data processing segmentdivided from a single data process to be routed to at least one of theplurality of networked computing processors; based at least partly onlatencies in execution of prior data processing requests routed by thesystem to each of the plurality of networked computing processors andthe available liquidity at each of the plurality of networked computingprocessors, determine a plurality of timing parameters, each of theplurality of timing parameters to be associated with a corresponding oneof the plurality of data processing segments, the plurality of timingparameters determined to cause synchronized execution of the pluralityof data processing segments by the plurality of networked computingprocessors; based on the timing parameters and networked computingprocessors associated with each of the plurality of data processes,determine a timing sequence for routing the data processing segments forall of the plurality of data processes; and route the plurality of dataprocessing segments to the plurality of corresponding networkedcomputing processors based on the timing sequence.
 2. The system ofclaim 1, wherein dividing the at least one data process into a pluralityof data processing segments comprises: selecting to which of theplurality networked computing resources at least one of the plurality ofdata processing segments is to be routed; and for each of the selectednetworked computing resources, determining a size of the correspondingat least one data process processing segments.
 3. The system of claim 2wherein the selection and the size determination are based at leastpartly on the available liquidity and a distribution of historicalexecution latencies associated with the plurality of computingresources.
 4. The system of claim 3, wherein the at least one processoris configured to: when the distribution for a particular networkedcomputing resource is a multimodal distribution: the plurality of dataprocessing segments include at least two data processing segments to berouted to the particular networked computing resource based on themultimodal distribution, and determine the timing parameters for the atleast two data processing segments based on the multimodal distribution.5. The system of claim 3, wherein dividing the at least one data processinto a plurality of data processing segments comprises dividing the atleast one data process into at least two data processing segments to berouted to the same networked computing resource; wherein the at leastone processor is configured to determine the timing parameters for eachof the at least two data processing segments.
 6. The system of claim 3,wherein the selection and the size determination are a function of thevariance of the distributions of historical execution latencies for theplurality of networked computing resources.
 7. The system of claim 1wherein the at least one processor is configured to determine whetherliquidity in addition to posted available liquidity is historicallyavailable at a particular networked computing resource; and whereindividing the at least one data process includes dividing the at leastone data process into at least two data processing segments to be routedto the particular networked computing resource, the at least two dataprocessing segments having timing parameters to target the postedavailable trade liquidity and the additional liquidity.
 8. A method forcoordinating processing of data by multiple networked computingresources, the method comprising: receiving from one or more datasources signals representing instructions for execution of a pluralityof data processes executable by a plurality of networked computingresources, the data processes representing a proposed trade in afinancial interest; obtaining data associated with available liquidityof the financial interest at each of the plurality of networkedcomputing resources; dividing each of the plurality of data processesinto a plurality of data processing segments, each data processingsegment divided from a single data process to be routed to at least oneof the plurality of networked computing processors; based at leastpartly on latencies in execution of prior data processing requestsrouted by the system to each of the plurality of networked computingprocessors and the available liquidity at each of the plurality ofnetworked computing processors, determining a plurality of timingparameters, each of the plurality of timing parameters to be associatedwith a corresponding one of the plurality of data processing segments,the plurality of timing parameters determined to cause synchronizedexecution of the plurality of data processing segments by the pluralityof networked computing processors; based on the timing parameters andnetworked computing processors associated with each of the plurality ofdata processes, determining a timing sequence for routing the dataprocessing segments for all of the plurality of data processes; androuting the plurality of data processing segments to the plurality ofcorresponding networked computing processors based on the timingsequence.
 9. The method of claim 8, wherein dividing the at least onedata process into a plurality of data processing segments comprises:selecting to which of the plurality networked computing resources atleast one of the plurality of data processing segments is to be routed;and for each of the selected networked computing resources, determininga size of the corresponding at least one data process processingsegments.
 10. The method of claim 9 wherein the selection and the sizedetermination are based at least partly on the available liquidity and adistribution of historical execution latencies associated with theplurality of computing resources.
 11. The method of claim 10, whereinwhen the distribution for a particular networked computing resource is amultimodal distribution: the plurality of data processing segmentsinclude at least two data processing segments to be routed to theparticular networked computing resource based on the multimodaldistribution, and the method comprises determining the timing parametersfor the at least two data processing segments based on the multimodaldistribution.
 12. The method of claim 10, wherein dividing the at leastone data process into a plurality of data processing segments comprisesdividing the at least one data process into at least two data processingsegments to be routed to the same networked computing resource; whereinthe method comprises determining the timing parameters for each of theat least two data processing segments.
 13. The method of claim 10,wherein the selection and the size determination are a function of thevariance of the distributions of historical execution latencies for theplurality of networked computing resources.
 14. The method of claim 8wherein the method comprises determining whether liquidity in additionto posted available liquidity is historically available at a particularnetworked computing resource; and wherein dividing the at least one dataprocess includes dividing the at least one data process into at leasttwo data processing segments to be routed to the particular networkedcomputing resource, the at least two data processing segments havingtiming parameters to target the posted available trade liquidity and theadditional liquidity.
 15. A computer-readable medium or media havingstored thereon instructions which when executed by at least oneprocessor, configure the at least one processor to: receive from one ormore data sources signals representing instructions for execution of aplurality of data processes executable by a plurality of networkedcomputing resources, the data processes representing a proposed trade ina financial interest; obtaining data associated with available liquidityof the financial interest at each of the plurality of networkedcomputing resources; divide each of the plurality of data processes intoa plurality of data processing segments, each data processing segmentdivided from a single data process to be routed to at least one of theplurality of networked computing processors; based at least partly onlatencies in execution of prior data processing requests routed by thesystem to each of the plurality of networked computing processors andthe available liquidity at each of the plurality of networked computingprocessors, determine a plurality of timing parameters, each of theplurality of timing parameters to be associated with a corresponding oneof the plurality of data processing segments, the plurality of timingparameters determined to cause synchronized execution of the pluralityof data processing segments by the plurality of networked computingprocessors; based on the timing parameters and networked computingprocessors associated with each of the plurality of data processes,determine a timing sequence for routing the data processing segments forall of the plurality of data processes; and route the plurality of dataprocessing segments to the plurality of corresponding networkedcomputing processors based on the timing sequence.